{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 时间序列"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',\n               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',\n               '2019-01-09', '2019-01-10', '2019-01-11', '2019-01-12',\n               '2019-01-13', '2019-01-14', '2019-01-15', '2019-01-16',\n               '2019-01-17', '2019-01-18', '2019-01-19', '2019-01-20',\n               '2019-01-21', '2019-01-22', '2019-01-23', '2019-01-24',\n               '2019-01-25', '2019-01-26', '2019-01-27', '2019-01-28',\n               '2019-01-29', '2019-01-30', '2019-01-31', '2019-02-01'],\n              dtype='datetime64[ns]', freq='D')"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.date_range(start=\"20190101\", end=\"20190201\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-01T15:56:04.437758600Z",
     "start_time": "2024-05-01T15:56:04.067747300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2024-04-11', '2024-04-12', '2024-04-15', '2024-04-16',\n               '2024-04-17', '2024-04-18', '2024-04-19', '2024-04-22',\n               '2024-04-23', '2024-04-24'],\n              dtype='datetime64[ns]', freq='B')"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20240411\",periods=10,freq='B')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-01T15:56:04.458625Z",
     "start_time": "2024-05-01T15:56:04.435726800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n               '2019-09-30', '2019-10-31'],\n              dtype='datetime64[ns]', freq='M')"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\",periods=10,freq='M')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-01T15:56:04.506949500Z",
     "start_time": "2024-05-01T15:56:04.449896800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',\n               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',\n               '2019-09-01', '2019-10-01'],\n              dtype='datetime64[ns]', freq='MS')"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\",periods=10,freq='MS')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-01T15:56:04.507976500Z",
     "start_time": "2024-05-01T15:56:04.466604600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2023-07-16', '2023-07-23', '2023-07-30', '2023-08-06',\n               '2023-08-13', '2023-08-20', '2023-08-27', '2023-09-03',\n               '2023-09-10', '2023-09-17'],\n              dtype='datetime64[ns]', freq='W-SUN')"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20230710\",periods=10,freq='W')  #拿每周的周日生成"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-01T15:56:04.508451Z",
     "start_time": "2024-05-01T15:56:04.482843200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3/11/2000\n",
      "1    3/12/2000\n",
      "2    3/13/2000\n",
      "3    3/11/2000\n",
      "4    3/12/2000\n",
      "5    3/13/2000\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 2)\n",
    "print(s)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-01T15:56:04.530392900Z",
     "start_time": "2024-05-01T15:56:04.495811Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lxxl\\AppData\\Local\\Temp\\ipykernel_16588\\845727718.py:2: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
      "  pd.to_datetime(s, infer_datetime_format=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": "0   2000-03-11\n1   2000-03-12\n2   2000-03-13\n3   2000-03-11\n4   2000-03-12\n5   2000-03-13\ndtype: datetime64[ns]"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#timeit可以统计执行耗时，to_datetime把字符串转为时间格式\n",
    "pd.to_datetime(s, infer_datetime_format=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-01T15:56:04.530392900Z",
     "start_time": "2024-05-01T15:56:04.510445800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 911数据\n",
    "### 三类事件：EMS、Fire、Traffic，统计这三类事件在每月发生的次数，画出随时间变化的折线图\n",
    "### 把字符串时间变为时间戳，画图时再把时间戳变为字符串"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "datetime64[ns]\n"
     ]
    },
    {
     "data": {
      "text/plain": "         lat        lng                                               desc  \\\n0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n\n       zip                    title           timeStamp                twp  \\\n0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:10:52        NEW HANOVER   \n1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n\n                         addr  e  \n0      REINDEER CT & DEAD END  1  \n1  BRIAR PATH & WHITEMARSH LN  1  \n2                    HAWS AVE  1  \n3          AIRY ST & SWEDE ST  1  \n4    CHERRYWOOD CT & DEAD END  1  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>desc</th>\n      <th>zip</th>\n      <th>title</th>\n      <th>timeStamp</th>\n      <th>twp</th>\n      <th>addr</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>40.297876</td>\n      <td>-75.581294</td>\n      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n      <td>19525.0</td>\n      <td>EMS: BACK PAINS/INJURY</td>\n      <td>2015-12-10 17:10:52</td>\n      <td>NEW HANOVER</td>\n      <td>REINDEER CT &amp; DEAD END</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>40.258061</td>\n      <td>-75.264680</td>\n      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n      <td>19446.0</td>\n      <td>EMS: DIABETIC EMERGENCY</td>\n      <td>2015-12-10 17:29:21</td>\n      <td>HATFIELD TOWNSHIP</td>\n      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40.121182</td>\n      <td>-75.351975</td>\n      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n      <td>19401.0</td>\n      <td>Fire: GAS-ODOR/LEAK</td>\n      <td>2015-12-10 14:39:21</td>\n      <td>NORRISTOWN</td>\n      <td>HAWS AVE</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>40.116153</td>\n      <td>-75.343513</td>\n      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n      <td>19401.0</td>\n      <td>EMS: CARDIAC EMERGENCY</td>\n      <td>2015-12-10 16:47:36</td>\n      <td>NORRISTOWN</td>\n      <td>AIRY ST &amp; SWEDE ST</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>40.251492</td>\n      <td>-75.603350</td>\n      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n      <td>NaN</td>\n      <td>EMS: DIZZINESS</td>\n      <td>2015-12-10 16:56:52</td>\n      <td>LOWER POTTSGROVE</td>\n      <td>CHERRYWOOD CT &amp; DEAD END</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 把时间字符串转为时间类型设置为索引\n",
    "df = pd.read_csv(\"./911.csv\")\n",
    "print(df[\"timeStamp\"].dtype)\n",
    "df[\"timeStamp\"] = pd.to_datetime(df[\"timeStamp\"])\n",
    "print(df[\"timeStamp\"].dtype)\n",
    "\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-05T05:42:39.011218600Z",
     "start_time": "2024-05-05T05:42:37.777102700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 添加列，表示分类\n",
    "temp_list = df[\"title\"].str.split(\": \").tolist() # 二维列表\n",
    "# .str.split()是一个Pandas库提供的方法，用于对Series对象进行字符串分割操作，并将结果存储为一个列表\n",
    "# .str.split(\": \")对\"title\"列中的每个元素使用冒号和空格\": \"作为分隔符进行分割。\n",
    "# 这意味着，如果\"title\"列中的某个元素是\"Emergency: Fire\"，那么分割后得到的列表将是[\"Emergency\", \"Fire\"]\n",
    "\n",
    "cate_list = [i[0] for i in temp_list] # i[0]就是EMS  Fire  Traffic\n",
    "# print(np.array(cate_list).reshape((df.shape[0], 1)))\n",
    "\n",
    "# 添加一列\n",
    "df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape(df.shape[0], 1))\n",
    "\n",
    "df.set_index(\"timeStamp\", inplace=True) #设置索引，时间戳，inplace=True表示在原df上修改"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "                           lat        lng  \\\ntimeStamp                                   \n2015-12-10 17:10:52  40.297876 -75.581294   \n2015-12-10 17:29:21  40.258061 -75.264680   \n2015-12-10 14:39:21  40.121182 -75.351975   \n2015-12-10 16:47:36  40.116153 -75.343513   \n2015-12-10 16:56:52  40.251492 -75.603350   \n\n                                                                  desc  \\\ntimeStamp                                                                \n2015-12-10 17:10:52  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n2015-12-10 14:39:21  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n2015-12-10 16:47:36  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n2015-12-10 16:56:52  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n\n                         zip                    title                twp  \\\ntimeStamp                                                                  \n2015-12-10 17:10:52  19525.0   EMS: BACK PAINS/INJURY        NEW HANOVER   \n2015-12-10 17:29:21  19446.0  EMS: DIABETIC EMERGENCY  HATFIELD TOWNSHIP   \n2015-12-10 14:39:21  19401.0      Fire: GAS-ODOR/LEAK         NORRISTOWN   \n2015-12-10 16:47:36  19401.0   EMS: CARDIAC EMERGENCY         NORRISTOWN   \n2015-12-10 16:56:52      NaN           EMS: DIZZINESS   LOWER POTTSGROVE   \n\n                                           addr  e  cate  \ntimeStamp                                                 \n2015-12-10 17:10:52      REINDEER CT & DEAD END  1   EMS  \n2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN  1   EMS  \n2015-12-10 14:39:21                    HAWS AVE  1  Fire  \n2015-12-10 16:47:36          AIRY ST & SWEDE ST  1   EMS  \n2015-12-10 16:56:52    CHERRYWOOD CT & DEAD END  1   EMS  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>desc</th>\n      <th>zip</th>\n      <th>title</th>\n      <th>twp</th>\n      <th>addr</th>\n      <th>e</th>\n      <th>cate</th>\n    </tr>\n    <tr>\n      <th>timeStamp</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2015-12-10 17:10:52</th>\n      <td>40.297876</td>\n      <td>-75.581294</td>\n      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n      <td>19525.0</td>\n      <td>EMS: BACK PAINS/INJURY</td>\n      <td>NEW HANOVER</td>\n      <td>REINDEER CT &amp; DEAD END</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 17:29:21</th>\n      <td>40.258061</td>\n      <td>-75.264680</td>\n      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n      <td>19446.0</td>\n      <td>EMS: DIABETIC EMERGENCY</td>\n      <td>HATFIELD TOWNSHIP</td>\n      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 14:39:21</th>\n      <td>40.121182</td>\n      <td>-75.351975</td>\n      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n      <td>19401.0</td>\n      <td>Fire: GAS-ODOR/LEAK</td>\n      <td>NORRISTOWN</td>\n      <td>HAWS AVE</td>\n      <td>1</td>\n      <td>Fire</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 16:47:36</th>\n      <td>40.116153</td>\n      <td>-75.343513</td>\n      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n      <td>19401.0</td>\n      <td>EMS: CARDIAC EMERGENCY</td>\n      <td>NORRISTOWN</td>\n      <td>AIRY ST &amp; SWEDE ST</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 16:56:52</th>\n      <td>40.251492</td>\n      <td>-75.603350</td>\n      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n      <td>NaN</td>\n      <td>EMS: DIZZINESS</td>\n      <td>LOWER POTTSGROVE</td>\n      <td>CHERRYWOOD CT &amp; DEAD END</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-05T05:41:30.560556400Z",
     "start_time": "2024-05-05T05:41:30.499717800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeStamp\n",
      "2015-12-31    3898\n",
      "2016-01-31    6063\n",
      "2016-02-29    5413\n",
      "2016-03-31    5832\n",
      "2016-04-30    5684\n",
      "2016-05-31    5730\n",
      "2016-06-30    5720\n",
      "2016-07-31    6029\n",
      "2016-08-31    6005\n",
      "2016-09-30    5750\n",
      "2016-10-31    6039\n",
      "2016-11-30    5838\n",
      "2016-12-31    6024\n",
      "2017-01-31    6082\n",
      "2017-02-28    5417\n",
      "2017-03-31    5913\n",
      "2017-04-30    5780\n",
      "2017-05-31    5948\n",
      "2017-06-30    6030\n",
      "2017-07-31    5974\n",
      "2017-08-31    5882\n",
      "2017-09-30    3789\n",
      "Freq: M, Name: title, dtype: int64\n",
      "----------------------------------------------------------------------------------------------------\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='M')\n",
      "====================================================================================================\n",
      "['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31', '2017-09-30']\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "timeStamp\n",
      "2015-12-31    1095\n",
      "2016-01-31    1904\n",
      "2016-02-29    1868\n",
      "2016-03-31    1589\n",
      "2016-04-30    1717\n",
      "2016-05-31    1573\n",
      "2016-06-30    1787\n",
      "2016-07-31    1898\n",
      "2016-08-31    1907\n",
      "2016-09-30    1793\n",
      "2016-10-31    1930\n",
      "2016-11-30    1765\n",
      "2016-12-31    1846\n",
      "2017-01-31    1658\n",
      "2017-02-28    1462\n",
      "2017-03-31    1634\n",
      "2017-04-30    1614\n",
      "2017-05-31    1670\n",
      "2017-06-30    1986\n",
      "2017-07-31    1754\n",
      "2017-08-31    1862\n",
      "2017-09-30    1120\n",
      "Freq: M, Name: title, dtype: int64\n",
      "----------------------------------------------------------------------------------------------------\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='M')\n",
      "====================================================================================================\n",
      "['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31', '2017-09-30']\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "timeStamp\n",
      "2015-12-31    2923\n",
      "2016-01-31    5129\n",
      "2016-02-29    4115\n",
      "2016-03-31    3638\n",
      "2016-04-30    3886\n",
      "2016-05-31    4071\n",
      "2016-06-30    4225\n",
      "2016-07-31    4161\n",
      "2016-08-31    3992\n",
      "2016-09-30    4126\n",
      "2016-10-31    4533\n",
      "2016-11-30    4488\n",
      "2016-12-31    4292\n",
      "2017-01-31    3865\n",
      "2017-02-28    3388\n",
      "2017-03-31    4137\n",
      "2017-04-30    3662\n",
      "2017-05-31    4101\n",
      "2017-06-30    4317\n",
      "2017-07-31    4040\n",
      "2017-08-31    4009\n",
      "2017-09-30    2367\n",
      "Freq: M, Name: title, dtype: int64\n",
      "----------------------------------------------------------------------------------------------------\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='M')\n",
      "====================================================================================================\n",
      "['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31', '2017-09-30']\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "# 分组,一图多线\n",
    "# resample降采样，按月统计,索引必须是时间类型，类似groupby\n",
    "for group_name, group_data in df.groupby(by=\"cate\"):\n",
    "\n",
    "    # 在时间序列数据中，降采样通常用于将数据从较细的时间粒度（如分钟、小时）聚合到较粗的时间粒度（如天、月、年）\n",
    "    # 相当于对样本进行了时间上的分组，这里跨度是M，返回一个分组对象\n",
    "    # resample方法只适用于DatetimeIndex类型的行索引。\n",
    "    # 如果行索引不是时间戳类型，即使列中有时间戳类型的数据，resample方法也不会生效，\n",
    "    # 因为Pandas无法识别非时间戳类型的索引来进行时间序列的降采样操作。\n",
    "    count_by_month = group_data.resample(\"M\").count()[\"title\"]  # 降采样\n",
    "    #print(group_data.resample(\"M\").count())\n",
    "    print(count_by_month)   # Series\n",
    "    print(\"--\"*50)\n",
    "\n",
    "    # 画图\n",
    "    _x = count_by_month.index # x轴就是时间\n",
    "    print(_x)\n",
    "    print(\"==\"*50)\n",
    "\n",
    "    _x = [i.strftime(\"%Y-%m-%d\") for i in _x]\n",
    "    # strftime(\"%Y%m%d\")是一个将时间戳（或 datetime 对象）转换为指定格式的字符串的方法。\n",
    "    # %Y 代表四位数的年份，%m 代表月份（01到12），%d 代表月份中的一天（01到31）。\n",
    "    # 因此，\"%Y-%m-%d\" 将时间戳格式化为 \"年-月-日\" 的字符串形式，例如 2021-03-01 代表 2021 年 3 月 1 日\n",
    "    print(_x)   # str类型\n",
    "    print(\"++\"*50)\n",
    "\n",
    "    _y = count_by_month.values # y轴就是对应事故发生次数，即values\n",
    "    plt.plot(_x, _y, label=group_name)\n",
    "\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.grid()\n",
    "plt.legend(loc=\"best\")  # 自动选择最佳的图例位置，以避免覆盖图表中的重要部分\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-02T02:13:10.232946400Z",
     "start_time": "2024-05-02T02:13:09.659734100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# pm2.5\n",
    "### 有两个部门，画出这两个区域的pm2.5值随星期的变化折线图\n",
    "### 把整数的时间变为时间戳"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeriodIndex(['2010-01-01 00:00', '2010-01-01 01:00', '2010-01-01 02:00',\n",
      "             '2010-01-01 03:00', '2010-01-01 04:00', '2010-01-01 05:00',\n",
      "             '2010-01-01 06:00', '2010-01-01 07:00', '2010-01-01 08:00',\n",
      "             '2010-01-01 09:00',\n",
      "             ...\n",
      "             '2015-12-31 14:00', '2015-12-31 15:00', '2015-12-31 16:00',\n",
      "             '2015-12-31 17:00', '2015-12-31 18:00', '2015-12-31 19:00',\n",
      "             '2015-12-31 20:00', '2015-12-31 21:00', '2015-12-31 22:00',\n",
      "             '2015-12-31 23:00'],\n",
      "            dtype='period[H]', length=52584)\n",
      "<class 'pandas.core.indexes.period.PeriodIndex'>\n",
      "--------------------------------------------------\n",
      "No                     int64\n",
      "year                   int64\n",
      "month                  int64\n",
      "day                    int64\n",
      "hour                   int64\n",
      "season                 int64\n",
      "PM_Dongsi            float64\n",
      "PM_Dongsihuan        float64\n",
      "PM_Nongzhanguan      float64\n",
      "PM_US Post           float64\n",
      "DEWP                 float64\n",
      "HUMI                 float64\n",
      "PRES                 float64\n",
      "TEMP                 float64\n",
      "cbwd                  object\n",
      "Iws                  float64\n",
      "precipitation        float64\n",
      "Iprec                float64\n",
      "datetime           period[H]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "file_path = \"./PM2.5/BeijingPM20100101_20151231.csv\"\n",
    "\n",
    "df = pd.read_csv(file_path)\n",
    "# print(df.head(10))\n",
    "\n",
    "# 把分开的时间字符串通过periodIndex的方法转化为pandas的时间类型\n",
    "period = pd.PeriodIndex(year=df[\"year\"], month=df[\"month\"], day=df[\"day\"],\n",
    "                        hour=df[\"hour\"], freq=\"H\")\n",
    "print(period)\n",
    "print(type(period)) # <class 'pandas.core.indexes.period.PeriodIndex'>\n",
    "print(\"-\"*50)\n",
    "\n",
    "df[\"datetime\"] = period\n",
    "#print(df.head(10))\n",
    "print(df.dtypes)\n",
    "\n",
    "# 把datetime 设置为行索引\n",
    "df.set_index(\"datetime\", inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-02T05:18:57.841226600Z",
     "start_time": "2024-05-02T05:18:57.095406100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  No  year  month  day  hour  season  PM_Dongsi  \\\n",
      "datetime                                                          \n",
      "2010-01-01 00:00   1  2010      1    1     0       4        NaN   \n",
      "2010-01-01 01:00   2  2010      1    1     1       4        NaN   \n",
      "2010-01-01 02:00   3  2010      1    1     2       4        NaN   \n",
      "\n",
      "                  PM_Dongsihuan  PM_Nongzhanguan  PM_US Post  DEWP  HUMI  \\\n",
      "datetime                                                                   \n",
      "2010-01-01 00:00            NaN              NaN         NaN -21.0  43.0   \n",
      "2010-01-01 01:00            NaN              NaN         NaN -21.0  47.0   \n",
      "2010-01-01 02:00            NaN              NaN         NaN -21.0  43.0   \n",
      "\n",
      "                    PRES  TEMP cbwd   Iws  precipitation  Iprec  \n",
      "datetime                                                         \n",
      "2010-01-01 00:00  1021.0 -11.0   NW  1.79            0.0    0.0  \n",
      "2010-01-01 01:00  1020.0 -12.0   NW  4.92            0.0    0.0  \n",
      "2010-01-01 02:00  1019.0 -11.0   NW  6.71            0.0    0.0  \n",
      "period[H]\n"
     ]
    }
   ],
   "source": [
    "print(df.head(3))\n",
    "print(\"-\"*50)\n",
    "\n",
    "# 进行降采样，行索引必须是pd的时间类型DatetimeIndex，\n",
    "# 这里行索引中的类型是period[H]，要先转为 datetime64[ns]\n",
    "print(df.index.dtype)\n",
    "\n",
    "df.index = pd.DatetimeIndex(df.index.to_timestamp())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-02T05:25:50.039202300Z",
     "start_time": "2024-05-02T05:25:49.997218500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2010-01-01 00:00:00', '2010-01-01 01:00:00',\n               '2010-01-01 02:00:00', '2010-01-01 03:00:00',\n               '2010-01-01 04:00:00', '2010-01-01 05:00:00',\n               '2010-01-01 06:00:00', '2010-01-01 07:00:00',\n               '2010-01-01 08:00:00', '2010-01-01 09:00:00',\n               ...\n               '2015-12-31 14:00:00', '2015-12-31 15:00:00',\n               '2015-12-31 16:00:00', '2015-12-31 17:00:00',\n               '2015-12-31 18:00:00', '2015-12-31 19:00:00',\n               '2015-12-31 20:00:00', '2015-12-31 21:00:00',\n               '2015-12-31 22:00:00', '2015-12-31 23:00:00'],\n              dtype='datetime64[ns]', name='datetime', length=52584, freq='H')"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:29:54.026966Z",
     "start_time": "2024-05-02T05:29:54.003768200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "                     No  year  month  day  hour  season  PM_Dongsi  \\\ndatetime                                                             \n2010-01-01 00:00:00   1  2010      1    1     0       4        NaN   \n2010-01-01 01:00:00   2  2010      1    1     1       4        NaN   \n2010-01-01 02:00:00   3  2010      1    1     2       4        NaN   \n\n                     PM_Dongsihuan  PM_Nongzhanguan  PM_US Post  DEWP  HUMI  \\\ndatetime                                                                      \n2010-01-01 00:00:00            NaN              NaN         NaN -21.0  43.0   \n2010-01-01 01:00:00            NaN              NaN         NaN -21.0  47.0   \n2010-01-01 02:00:00            NaN              NaN         NaN -21.0  43.0   \n\n                       PRES  TEMP cbwd   Iws  precipitation  Iprec  \ndatetime                                                            \n2010-01-01 00:00:00  1021.0 -11.0   NW  1.79            0.0    0.0  \n2010-01-01 01:00:00  1020.0 -12.0   NW  4.92            0.0    0.0  \n2010-01-01 02:00:00  1019.0 -11.0   NW  6.71            0.0    0.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>No</th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>hour</th>\n      <th>season</th>\n      <th>PM_Dongsi</th>\n      <th>PM_Dongsihuan</th>\n      <th>PM_Nongzhanguan</th>\n      <th>PM_US Post</th>\n      <th>DEWP</th>\n      <th>HUMI</th>\n      <th>PRES</th>\n      <th>TEMP</th>\n      <th>cbwd</th>\n      <th>Iws</th>\n      <th>precipitation</th>\n      <th>Iprec</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2010-01-01 00:00:00</th>\n      <td>1</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-21.0</td>\n      <td>43.0</td>\n      <td>1021.0</td>\n      <td>-11.0</td>\n      <td>NW</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2010-01-01 01:00:00</th>\n      <td>2</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-21.0</td>\n      <td>47.0</td>\n      <td>1020.0</td>\n      <td>-12.0</td>\n      <td>NW</td>\n      <td>4.92</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2010-01-01 02:00:00</th>\n      <td>3</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-21.0</td>\n      <td>43.0</td>\n      <td>1019.0</td>\n      <td>-11.0</td>\n      <td>NW</td>\n      <td>6.71</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:29:15.592277700Z",
     "start_time": "2024-05-02T05:29:15.559364200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 每7天为一组，组名为这七天的第一天"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-01-01 00:00:00         NaN              NaN\n",
      "2010-01-01 01:00:00         NaN              NaN\n",
      "2010-01-01 02:00:00         NaN              NaN\n",
      "2010-01-01 03:00:00         NaN              NaN\n",
      "2010-01-01 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2010-01-07 19:00:00       106.0              NaN\n",
      "2010-01-07 20:00:00       159.0              NaN\n",
      "2010-01-07 21:00:00       198.0              NaN\n",
      "2010-01-07 22:00:00       190.0              NaN\n",
      "2010-01-07 23:00:00       210.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-01-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-01-08 00:00:00       195.0              NaN\n",
      "2010-01-08 01:00:00       275.0              NaN\n",
      "2010-01-08 02:00:00       164.0              NaN\n",
      "2010-01-08 03:00:00       110.0              NaN\n",
      "2010-01-08 04:00:00       100.0              NaN\n",
      "...                         ...              ...\n",
      "2010-01-14 19:00:00        49.0              NaN\n",
      "2010-01-14 20:00:00        33.0              NaN\n",
      "2010-01-14 21:00:00        27.0              NaN\n",
      "2010-01-14 22:00:00        20.0              NaN\n",
      "2010-01-14 23:00:00        36.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-01-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-01-15 00:00:00        37.0              NaN\n",
      "2010-01-15 01:00:00        30.0              NaN\n",
      "2010-01-15 02:00:00        18.0              NaN\n",
      "2010-01-15 03:00:00        21.0              NaN\n",
      "2010-01-15 04:00:00        13.0              NaN\n",
      "...                         ...              ...\n",
      "2010-01-21 19:00:00        45.0              NaN\n",
      "2010-01-21 20:00:00        61.0              NaN\n",
      "2010-01-21 21:00:00        72.0              NaN\n",
      "2010-01-21 22:00:00        38.0              NaN\n",
      "2010-01-21 23:00:00        35.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-01-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-01-22 00:00:00        29.0              NaN\n",
      "2010-01-22 01:00:00        26.0              NaN\n",
      "2010-01-22 02:00:00        18.0              NaN\n",
      "2010-01-22 03:00:00        15.0              NaN\n",
      "2010-01-22 04:00:00        15.0              NaN\n",
      "...                         ...              ...\n",
      "2010-01-28 19:00:00        16.0              NaN\n",
      "2010-01-28 20:00:00        25.0              NaN\n",
      "2010-01-28 21:00:00        20.0              NaN\n",
      "2010-01-28 22:00:00        25.0              NaN\n",
      "2010-01-28 23:00:00        19.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-01-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-01-29 00:00:00        24.0              NaN\n",
      "2010-01-29 01:00:00        25.0              NaN\n",
      "2010-01-29 02:00:00        20.0              NaN\n",
      "2010-01-29 03:00:00        24.0              NaN\n",
      "2010-01-29 04:00:00        17.0              NaN\n",
      "...                         ...              ...\n",
      "2010-02-04 19:00:00        48.0              NaN\n",
      "2010-02-04 20:00:00        59.0              NaN\n",
      "2010-02-04 21:00:00        82.0              NaN\n",
      "2010-02-04 22:00:00        63.0              NaN\n",
      "2010-02-04 23:00:00        48.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-02-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-02-05 00:00:00        54.0              NaN\n",
      "2010-02-05 01:00:00        42.0              NaN\n",
      "2010-02-05 02:00:00        47.0              NaN\n",
      "2010-02-05 03:00:00        87.0              NaN\n",
      "2010-02-05 04:00:00       105.0              NaN\n",
      "...                         ...              ...\n",
      "2010-02-11 19:00:00        20.0              NaN\n",
      "2010-02-11 20:00:00        24.0              NaN\n",
      "2010-02-11 21:00:00        19.0              NaN\n",
      "2010-02-11 22:00:00        13.0              NaN\n",
      "2010-02-11 23:00:00        17.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-02-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-02-12 00:00:00        22.0              NaN\n",
      "2010-02-12 01:00:00        16.0              NaN\n",
      "2010-02-12 02:00:00        16.0              NaN\n",
      "2010-02-12 03:00:00        11.0              NaN\n",
      "2010-02-12 04:00:00        10.0              NaN\n",
      "...                         ...              ...\n",
      "2010-02-18 19:00:00       115.0              NaN\n",
      "2010-02-18 20:00:00       265.0              NaN\n",
      "2010-02-18 21:00:00       236.0              NaN\n",
      "2010-02-18 22:00:00       296.0              NaN\n",
      "2010-02-18 23:00:00       159.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-02-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-02-19 00:00:00       119.0              NaN\n",
      "2010-02-19 01:00:00       115.0              NaN\n",
      "2010-02-19 02:00:00       168.0              NaN\n",
      "2010-02-19 03:00:00       177.0              NaN\n",
      "2010-02-19 04:00:00       191.0              NaN\n",
      "...                         ...              ...\n",
      "2010-02-25 19:00:00       130.0              NaN\n",
      "2010-02-25 20:00:00       118.0              NaN\n",
      "2010-02-25 21:00:00        56.0              NaN\n",
      "2010-02-25 22:00:00        55.0              NaN\n",
      "2010-02-25 23:00:00        52.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-02-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-02-26 00:00:00        46.0              NaN\n",
      "2010-02-26 01:00:00        46.0              NaN\n",
      "2010-02-26 02:00:00        45.0              NaN\n",
      "2010-02-26 03:00:00        51.0              NaN\n",
      "2010-02-26 04:00:00        53.0              NaN\n",
      "...                         ...              ...\n",
      "2010-03-04 19:00:00       256.0              NaN\n",
      "2010-03-04 20:00:00       255.0              NaN\n",
      "2010-03-04 21:00:00       263.0              NaN\n",
      "2010-03-04 22:00:00       269.0              NaN\n",
      "2010-03-04 23:00:00       248.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-03-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-03-05 00:00:00        89.0              NaN\n",
      "2010-03-05 01:00:00        15.0              NaN\n",
      "2010-03-05 02:00:00        19.0              NaN\n",
      "2010-03-05 03:00:00        15.0              NaN\n",
      "2010-03-05 04:00:00        13.0              NaN\n",
      "...                         ...              ...\n",
      "2010-03-11 19:00:00       222.0              NaN\n",
      "2010-03-11 20:00:00       216.0              NaN\n",
      "2010-03-11 21:00:00       219.0              NaN\n",
      "2010-03-11 22:00:00       223.0              NaN\n",
      "2010-03-11 23:00:00       213.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-03-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-03-12 00:00:00       314.0              NaN\n",
      "2010-03-12 01:00:00        59.0              NaN\n",
      "2010-03-12 02:00:00        38.0              NaN\n",
      "2010-03-12 03:00:00        29.0              NaN\n",
      "2010-03-12 04:00:00        27.0              NaN\n",
      "...                         ...              ...\n",
      "2010-03-18 19:00:00       209.0              NaN\n",
      "2010-03-18 20:00:00       226.0              NaN\n",
      "2010-03-18 21:00:00       222.0              NaN\n",
      "2010-03-18 22:00:00       239.0              NaN\n",
      "2010-03-18 23:00:00       241.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-03-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-03-19 00:00:00       249.0              NaN\n",
      "2010-03-19 01:00:00       248.0              NaN\n",
      "2010-03-19 02:00:00       247.0              NaN\n",
      "2010-03-19 03:00:00       228.0              NaN\n",
      "2010-03-19 04:00:00       225.0              NaN\n",
      "...                         ...              ...\n",
      "2010-03-25 19:00:00        29.0              NaN\n",
      "2010-03-25 20:00:00        33.0              NaN\n",
      "2010-03-25 21:00:00        34.0              NaN\n",
      "2010-03-25 22:00:00        59.0              NaN\n",
      "2010-03-25 23:00:00        63.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-03-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-03-26 00:00:00        67.0              NaN\n",
      "2010-03-26 01:00:00        62.0              NaN\n",
      "2010-03-26 02:00:00        72.0              NaN\n",
      "2010-03-26 03:00:00        48.0              NaN\n",
      "2010-03-26 04:00:00        70.0              NaN\n",
      "...                         ...              ...\n",
      "2010-04-01 19:00:00        24.0              NaN\n",
      "2010-04-01 20:00:00        20.0              NaN\n",
      "2010-04-01 21:00:00        54.0              NaN\n",
      "2010-04-01 22:00:00        39.0              NaN\n",
      "2010-04-01 23:00:00        27.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-04-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-04-02 00:00:00        52.0              NaN\n",
      "2010-04-02 01:00:00        41.0              NaN\n",
      "2010-04-02 02:00:00        35.0              NaN\n",
      "2010-04-02 03:00:00        44.0              NaN\n",
      "2010-04-02 04:00:00        45.0              NaN\n",
      "...                         ...              ...\n",
      "2010-04-08 19:00:00       158.0              NaN\n",
      "2010-04-08 20:00:00       141.0              NaN\n",
      "2010-04-08 21:00:00       124.0              NaN\n",
      "2010-04-08 22:00:00       131.0              NaN\n",
      "2010-04-08 23:00:00       150.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-04-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-04-09 00:00:00       137.0              NaN\n",
      "2010-04-09 01:00:00       128.0              NaN\n",
      "2010-04-09 02:00:00       103.0              NaN\n",
      "2010-04-09 03:00:00        60.0              NaN\n",
      "2010-04-09 04:00:00        50.0              NaN\n",
      "...                         ...              ...\n",
      "2010-04-15 19:00:00       212.0              NaN\n",
      "2010-04-15 20:00:00       220.0              NaN\n",
      "2010-04-15 21:00:00       237.0              NaN\n",
      "2010-04-15 22:00:00       225.0              NaN\n",
      "2010-04-15 23:00:00       235.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-04-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-04-16 00:00:00       239.0              NaN\n",
      "2010-04-16 01:00:00       244.0              NaN\n",
      "2010-04-16 02:00:00       255.0              NaN\n",
      "2010-04-16 03:00:00       247.0              NaN\n",
      "2010-04-16 04:00:00       253.0              NaN\n",
      "...                         ...              ...\n",
      "2010-04-22 19:00:00        33.0              NaN\n",
      "2010-04-22 20:00:00        47.0              NaN\n",
      "2010-04-22 21:00:00        41.0              NaN\n",
      "2010-04-22 22:00:00        47.0              NaN\n",
      "2010-04-22 23:00:00        37.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-04-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-04-23 00:00:00        41.0              NaN\n",
      "2010-04-23 01:00:00        60.0              NaN\n",
      "2010-04-23 02:00:00        62.0              NaN\n",
      "2010-04-23 03:00:00        36.0              NaN\n",
      "2010-04-23 04:00:00        31.0              NaN\n",
      "...                         ...              ...\n",
      "2010-04-29 19:00:00        18.0              NaN\n",
      "2010-04-29 20:00:00        19.0              NaN\n",
      "2010-04-29 21:00:00        15.0              NaN\n",
      "2010-04-29 22:00:00        19.0              NaN\n",
      "2010-04-29 23:00:00        27.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-04-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-04-30 00:00:00        21.0              NaN\n",
      "2010-04-30 01:00:00        24.0              NaN\n",
      "2010-04-30 02:00:00        32.0              NaN\n",
      "2010-04-30 03:00:00        40.0              NaN\n",
      "2010-04-30 04:00:00        36.0              NaN\n",
      "...                         ...              ...\n",
      "2010-05-06 19:00:00        32.0              NaN\n",
      "2010-05-06 20:00:00        35.0              NaN\n",
      "2010-05-06 21:00:00        35.0              NaN\n",
      "2010-05-06 22:00:00        41.0              NaN\n",
      "2010-05-06 23:00:00        48.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-05-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-05-07 00:00:00        50.0              NaN\n",
      "2010-05-07 01:00:00        52.0              NaN\n",
      "2010-05-07 02:00:00        57.0              NaN\n",
      "2010-05-07 03:00:00        64.0              NaN\n",
      "2010-05-07 04:00:00        66.0              NaN\n",
      "...                         ...              ...\n",
      "2010-05-13 19:00:00        68.0              NaN\n",
      "2010-05-13 20:00:00        64.0              NaN\n",
      "2010-05-13 21:00:00        69.0              NaN\n",
      "2010-05-13 22:00:00        64.0              NaN\n",
      "2010-05-13 23:00:00        60.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-05-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-05-14 00:00:00        65.0              NaN\n",
      "2010-05-14 01:00:00        61.0              NaN\n",
      "2010-05-14 02:00:00        83.0              NaN\n",
      "2010-05-14 03:00:00       112.0              NaN\n",
      "2010-05-14 04:00:00        99.0              NaN\n",
      "...                         ...              ...\n",
      "2010-05-20 19:00:00        43.0              NaN\n",
      "2010-05-20 20:00:00        54.0              NaN\n",
      "2010-05-20 21:00:00        68.0              NaN\n",
      "2010-05-20 22:00:00        78.0              NaN\n",
      "2010-05-20 23:00:00        93.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-05-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-05-21 00:00:00        91.0              NaN\n",
      "2010-05-21 01:00:00        96.0              NaN\n",
      "2010-05-21 02:00:00       106.0              NaN\n",
      "2010-05-21 03:00:00       101.0              NaN\n",
      "2010-05-21 04:00:00        90.0              NaN\n",
      "...                         ...              ...\n",
      "2010-05-27 19:00:00         NaN              NaN\n",
      "2010-05-27 20:00:00         NaN              NaN\n",
      "2010-05-27 21:00:00         NaN              NaN\n",
      "2010-05-27 22:00:00         NaN              NaN\n",
      "2010-05-27 23:00:00        90.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-05-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-05-28 00:00:00        89.0              NaN\n",
      "2010-05-28 01:00:00       100.0              NaN\n",
      "2010-05-28 02:00:00        86.0              NaN\n",
      "2010-05-28 03:00:00        83.0              NaN\n",
      "2010-05-28 04:00:00        94.0              NaN\n",
      "...                         ...              ...\n",
      "2010-06-03 19:00:00        85.0              NaN\n",
      "2010-06-03 20:00:00        90.0              NaN\n",
      "2010-06-03 21:00:00        81.0              NaN\n",
      "2010-06-03 22:00:00        86.0              NaN\n",
      "2010-06-03 23:00:00        79.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-06-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-06-04 00:00:00        71.0              NaN\n",
      "2010-06-04 01:00:00        64.0              NaN\n",
      "2010-06-04 02:00:00        63.0              NaN\n",
      "2010-06-04 03:00:00        76.0              NaN\n",
      "2010-06-04 04:00:00        89.0              NaN\n",
      "...                         ...              ...\n",
      "2010-06-10 19:00:00        53.0              NaN\n",
      "2010-06-10 20:00:00        58.0              NaN\n",
      "2010-06-10 21:00:00        78.0              NaN\n",
      "2010-06-10 22:00:00        80.0              NaN\n",
      "2010-06-10 23:00:00        88.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-06-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-06-11 00:00:00        97.0              NaN\n",
      "2010-06-11 01:00:00        77.0              NaN\n",
      "2010-06-11 02:00:00        72.0              NaN\n",
      "2010-06-11 03:00:00        78.0              NaN\n",
      "2010-06-11 04:00:00        81.0              NaN\n",
      "...                         ...              ...\n",
      "2010-06-17 19:00:00        37.0              NaN\n",
      "2010-06-17 20:00:00        39.0              NaN\n",
      "2010-06-17 21:00:00        50.0              NaN\n",
      "2010-06-17 22:00:00        49.0              NaN\n",
      "2010-06-17 23:00:00        41.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-06-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-06-18 00:00:00        34.0              NaN\n",
      "2010-06-18 01:00:00        58.0              NaN\n",
      "2010-06-18 02:00:00        57.0              NaN\n",
      "2010-06-18 03:00:00        39.0              NaN\n",
      "2010-06-18 04:00:00        40.0              NaN\n",
      "...                         ...              ...\n",
      "2010-06-24 19:00:00        64.0              NaN\n",
      "2010-06-24 20:00:00        81.0              NaN\n",
      "2010-06-24 21:00:00        83.0              NaN\n",
      "2010-06-24 22:00:00        98.0              NaN\n",
      "2010-06-24 23:00:00       114.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-06-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-06-25 00:00:00       169.0              NaN\n",
      "2010-06-25 01:00:00       231.0              NaN\n",
      "2010-06-25 02:00:00       214.0              NaN\n",
      "2010-06-25 03:00:00       188.0              NaN\n",
      "2010-06-25 04:00:00       165.0              NaN\n",
      "...                         ...              ...\n",
      "2010-07-01 19:00:00        40.0              NaN\n",
      "2010-07-01 20:00:00        36.0              NaN\n",
      "2010-07-01 21:00:00        27.0              NaN\n",
      "2010-07-01 22:00:00        37.0              NaN\n",
      "2010-07-01 23:00:00        46.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-07-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-07-02 00:00:00        43.0              NaN\n",
      "2010-07-02 01:00:00        37.0              NaN\n",
      "2010-07-02 02:00:00        40.0              NaN\n",
      "2010-07-02 03:00:00        45.0              NaN\n",
      "2010-07-02 04:00:00        17.0              NaN\n",
      "...                         ...              ...\n",
      "2010-07-08 19:00:00        32.0              NaN\n",
      "2010-07-08 20:00:00        33.0              NaN\n",
      "2010-07-08 21:00:00        70.0              NaN\n",
      "2010-07-08 22:00:00        73.0              NaN\n",
      "2010-07-08 23:00:00        69.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-07-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-07-09 00:00:00        86.0              NaN\n",
      "2010-07-09 01:00:00        81.0              NaN\n",
      "2010-07-09 02:00:00        67.0              NaN\n",
      "2010-07-09 03:00:00        69.0              NaN\n",
      "2010-07-09 04:00:00        69.0              NaN\n",
      "...                         ...              ...\n",
      "2010-07-15 19:00:00       235.0              NaN\n",
      "2010-07-15 20:00:00       286.0              NaN\n",
      "2010-07-15 21:00:00       264.0              NaN\n",
      "2010-07-15 22:00:00       266.0              NaN\n",
      "2010-07-15 23:00:00       213.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-07-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-07-16 00:00:00       196.0              NaN\n",
      "2010-07-16 01:00:00       185.0              NaN\n",
      "2010-07-16 02:00:00       199.0              NaN\n",
      "2010-07-16 03:00:00       225.0              NaN\n",
      "2010-07-16 04:00:00       231.0              NaN\n",
      "...                         ...              ...\n",
      "2010-07-22 19:00:00       194.0              NaN\n",
      "2010-07-22 20:00:00       176.0              NaN\n",
      "2010-07-22 21:00:00       170.0              NaN\n",
      "2010-07-22 22:00:00       169.0              NaN\n",
      "2010-07-22 23:00:00       189.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-07-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-07-23 00:00:00       207.0              NaN\n",
      "2010-07-23 01:00:00       202.0              NaN\n",
      "2010-07-23 02:00:00       172.0              NaN\n",
      "2010-07-23 03:00:00       247.0              NaN\n",
      "2010-07-23 04:00:00       233.0              NaN\n",
      "...                         ...              ...\n",
      "2010-07-29 19:00:00       277.0              NaN\n",
      "2010-07-29 20:00:00       276.0              NaN\n",
      "2010-07-29 21:00:00       246.0              NaN\n",
      "2010-07-29 22:00:00       285.0              NaN\n",
      "2010-07-29 23:00:00       246.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-07-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-07-30 00:00:00       230.0              NaN\n",
      "2010-07-30 01:00:00       224.0              NaN\n",
      "2010-07-30 02:00:00       210.0              NaN\n",
      "2010-07-30 03:00:00       216.0              NaN\n",
      "2010-07-30 04:00:00       199.0              NaN\n",
      "...                         ...              ...\n",
      "2010-08-05 19:00:00        18.0              NaN\n",
      "2010-08-05 20:00:00        12.0              NaN\n",
      "2010-08-05 21:00:00        12.0              NaN\n",
      "2010-08-05 22:00:00        11.0              NaN\n",
      "2010-08-05 23:00:00        11.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-08-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-08-06 00:00:00        12.0              NaN\n",
      "2010-08-06 01:00:00        10.0              NaN\n",
      "2010-08-06 02:00:00        15.0              NaN\n",
      "2010-08-06 03:00:00        16.0              NaN\n",
      "2010-08-06 04:00:00        14.0              NaN\n",
      "...                         ...              ...\n",
      "2010-08-12 19:00:00        47.0              NaN\n",
      "2010-08-12 20:00:00        83.0              NaN\n",
      "2010-08-12 21:00:00        96.0              NaN\n",
      "2010-08-12 22:00:00       111.0              NaN\n",
      "2010-08-12 23:00:00       100.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-08-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-08-13 00:00:00        84.0              NaN\n",
      "2010-08-13 01:00:00        88.0              NaN\n",
      "2010-08-13 02:00:00        79.0              NaN\n",
      "2010-08-13 03:00:00        75.0              NaN\n",
      "2010-08-13 04:00:00        88.0              NaN\n",
      "...                         ...              ...\n",
      "2010-08-19 19:00:00       134.0              NaN\n",
      "2010-08-19 20:00:00       137.0              NaN\n",
      "2010-08-19 21:00:00       154.0              NaN\n",
      "2010-08-19 22:00:00       210.0              NaN\n",
      "2010-08-19 23:00:00       195.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-08-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-08-20 00:00:00       154.0              NaN\n",
      "2010-08-20 01:00:00       152.0              NaN\n",
      "2010-08-20 02:00:00       164.0              NaN\n",
      "2010-08-20 03:00:00       165.0              NaN\n",
      "2010-08-20 04:00:00       126.0              NaN\n",
      "...                         ...              ...\n",
      "2010-08-26 19:00:00       101.0              NaN\n",
      "2010-08-26 20:00:00        26.0              NaN\n",
      "2010-08-26 21:00:00        22.0              NaN\n",
      "2010-08-26 22:00:00        25.0              NaN\n",
      "2010-08-26 23:00:00        33.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-08-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-08-27 00:00:00        33.0              NaN\n",
      "2010-08-27 01:00:00        36.0              NaN\n",
      "2010-08-27 02:00:00        33.0              NaN\n",
      "2010-08-27 03:00:00        43.0              NaN\n",
      "2010-08-27 04:00:00        46.0              NaN\n",
      "...                         ...              ...\n",
      "2010-09-02 19:00:00       191.0              NaN\n",
      "2010-09-02 20:00:00       211.0              NaN\n",
      "2010-09-02 21:00:00       224.0              NaN\n",
      "2010-09-02 22:00:00       249.0              NaN\n",
      "2010-09-02 23:00:00       254.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-09-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-09-03 00:00:00       284.0              NaN\n",
      "2010-09-03 01:00:00       228.0              NaN\n",
      "2010-09-03 02:00:00       135.0              NaN\n",
      "2010-09-03 03:00:00        94.0              NaN\n",
      "2010-09-03 04:00:00       101.0              NaN\n",
      "...                         ...              ...\n",
      "2010-09-09 19:00:00       131.0              NaN\n",
      "2010-09-09 20:00:00       144.0              NaN\n",
      "2010-09-09 21:00:00       147.0              NaN\n",
      "2010-09-09 22:00:00       160.0              NaN\n",
      "2010-09-09 23:00:00       175.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-09-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-09-10 00:00:00       176.0              NaN\n",
      "2010-09-10 01:00:00       193.0              NaN\n",
      "2010-09-10 02:00:00       200.0              NaN\n",
      "2010-09-10 03:00:00       189.0              NaN\n",
      "2010-09-10 04:00:00       108.0              NaN\n",
      "...                         ...              ...\n",
      "2010-09-16 19:00:00       170.0              NaN\n",
      "2010-09-16 20:00:00       153.0              NaN\n",
      "2010-09-16 21:00:00       104.0              NaN\n",
      "2010-09-16 22:00:00       136.0              NaN\n",
      "2010-09-16 23:00:00       117.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-09-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-09-17 00:00:00        94.0              NaN\n",
      "2010-09-17 01:00:00       112.0              NaN\n",
      "2010-09-17 02:00:00        71.0              NaN\n",
      "2010-09-17 03:00:00        89.0              NaN\n",
      "2010-09-17 04:00:00        84.0              NaN\n",
      "...                         ...              ...\n",
      "2010-09-23 19:00:00         NaN              NaN\n",
      "2010-09-23 20:00:00         NaN              NaN\n",
      "2010-09-23 21:00:00         NaN              NaN\n",
      "2010-09-23 22:00:00         NaN              NaN\n",
      "2010-09-23 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-09-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-09-24 00:00:00         NaN              NaN\n",
      "2010-09-24 01:00:00         NaN              NaN\n",
      "2010-09-24 02:00:00         NaN              NaN\n",
      "2010-09-24 03:00:00         NaN              NaN\n",
      "2010-09-24 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2010-09-30 19:00:00         NaN              NaN\n",
      "2010-09-30 20:00:00       209.0              NaN\n",
      "2010-09-30 21:00:00       204.0              NaN\n",
      "2010-09-30 22:00:00       222.0              NaN\n",
      "2010-09-30 23:00:00       231.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-10-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-10-01 00:00:00       268.0              NaN\n",
      "2010-10-01 01:00:00       258.0              NaN\n",
      "2010-10-01 02:00:00       196.0              NaN\n",
      "2010-10-01 03:00:00       192.0              NaN\n",
      "2010-10-01 04:00:00       165.0              NaN\n",
      "...                         ...              ...\n",
      "2010-10-07 19:00:00       327.0              NaN\n",
      "2010-10-07 20:00:00       365.0              NaN\n",
      "2010-10-07 21:00:00       391.0              NaN\n",
      "2010-10-07 22:00:00       423.0              NaN\n",
      "2010-10-07 23:00:00       429.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-10-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-10-08 00:00:00       439.0              NaN\n",
      "2010-10-08 01:00:00       458.0              NaN\n",
      "2010-10-08 02:00:00       436.0              NaN\n",
      "2010-10-08 03:00:00       431.0              NaN\n",
      "2010-10-08 04:00:00       408.0              NaN\n",
      "...                         ...              ...\n",
      "2010-10-14 19:00:00        22.0              NaN\n",
      "2010-10-14 20:00:00        13.0              NaN\n",
      "2010-10-14 21:00:00        21.0              NaN\n",
      "2010-10-14 22:00:00        30.0              NaN\n",
      "2010-10-14 23:00:00        19.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-10-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-10-15 00:00:00        19.0              NaN\n",
      "2010-10-15 01:00:00        15.0              NaN\n",
      "2010-10-15 02:00:00        18.0              NaN\n",
      "2010-10-15 03:00:00        56.0              NaN\n",
      "2010-10-15 04:00:00        38.0              NaN\n",
      "...                         ...              ...\n",
      "2010-10-21 19:00:00       161.0              NaN\n",
      "2010-10-21 20:00:00       180.0              NaN\n",
      "2010-10-21 21:00:00       179.0              NaN\n",
      "2010-10-21 22:00:00       179.0              NaN\n",
      "2010-10-21 23:00:00       184.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-10-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-10-22 00:00:00       176.0              NaN\n",
      "2010-10-22 01:00:00       164.0              NaN\n",
      "2010-10-22 02:00:00       152.0              NaN\n",
      "2010-10-22 03:00:00       143.0              NaN\n",
      "2010-10-22 04:00:00       142.0              NaN\n",
      "...                         ...              ...\n",
      "2010-10-28 19:00:00        48.0              NaN\n",
      "2010-10-28 20:00:00        41.0              NaN\n",
      "2010-10-28 21:00:00        41.0              NaN\n",
      "2010-10-28 22:00:00        50.0              NaN\n",
      "2010-10-28 23:00:00        47.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-10-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-10-29 00:00:00        67.0              NaN\n",
      "2010-10-29 01:00:00        76.0              NaN\n",
      "2010-10-29 02:00:00        79.0              NaN\n",
      "2010-10-29 03:00:00        72.0              NaN\n",
      "2010-10-29 04:00:00        88.0              NaN\n",
      "...                         ...              ...\n",
      "2010-11-04 19:00:00        63.0              NaN\n",
      "2010-11-04 20:00:00        91.0              NaN\n",
      "2010-11-04 21:00:00       119.0              NaN\n",
      "2010-11-04 22:00:00       136.0              NaN\n",
      "2010-11-04 23:00:00       144.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-11-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-11-05 00:00:00       148.0              NaN\n",
      "2010-11-05 01:00:00       130.0              NaN\n",
      "2010-11-05 02:00:00        98.0              NaN\n",
      "2010-11-05 03:00:00        70.0              NaN\n",
      "2010-11-05 04:00:00        95.0              NaN\n",
      "...                         ...              ...\n",
      "2010-11-11 19:00:00        11.0              NaN\n",
      "2010-11-11 20:00:00         8.0              NaN\n",
      "2010-11-11 21:00:00         6.0              NaN\n",
      "2010-11-11 22:00:00         7.0              NaN\n",
      "2010-11-11 23:00:00         8.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-11-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-11-12 00:00:00        11.0              NaN\n",
      "2010-11-12 01:00:00        10.0              NaN\n",
      "2010-11-12 02:00:00         9.0              NaN\n",
      "2010-11-12 03:00:00        12.0              NaN\n",
      "2010-11-12 04:00:00        30.0              NaN\n",
      "...                         ...              ...\n",
      "2010-11-18 19:00:00       503.0              NaN\n",
      "2010-11-18 20:00:00       470.0              NaN\n",
      "2010-11-18 21:00:00       480.0              NaN\n",
      "2010-11-18 22:00:00       482.0              NaN\n",
      "2010-11-18 23:00:00       535.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-11-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-11-19 00:00:00       569.0              NaN\n",
      "2010-11-19 01:00:00       562.0              NaN\n",
      "2010-11-19 02:00:00       485.0              NaN\n",
      "2010-11-19 03:00:00       488.0              NaN\n",
      "2010-11-19 04:00:00       470.0              NaN\n",
      "...                         ...              ...\n",
      "2010-11-25 19:00:00       104.0              NaN\n",
      "2010-11-25 20:00:00       146.0              NaN\n",
      "2010-11-25 21:00:00       177.0              NaN\n",
      "2010-11-25 22:00:00       168.0              NaN\n",
      "2010-11-25 23:00:00       142.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-11-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-11-26 00:00:00       120.0              NaN\n",
      "2010-11-26 01:00:00        94.0              NaN\n",
      "2010-11-26 02:00:00        89.0              NaN\n",
      "2010-11-26 03:00:00        85.0              NaN\n",
      "2010-11-26 04:00:00        62.0              NaN\n",
      "...                         ...              ...\n",
      "2010-12-02 19:00:00        21.0              NaN\n",
      "2010-12-02 20:00:00        17.0              NaN\n",
      "2010-12-02 21:00:00        25.0              NaN\n",
      "2010-12-02 22:00:00        23.0              NaN\n",
      "2010-12-02 23:00:00        15.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-12-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-12-03 00:00:00        23.0              NaN\n",
      "2010-12-03 01:00:00        22.0              NaN\n",
      "2010-12-03 02:00:00        16.0              NaN\n",
      "2010-12-03 03:00:00        11.0              NaN\n",
      "2010-12-03 04:00:00        13.0              NaN\n",
      "...                         ...              ...\n",
      "2010-12-09 19:00:00       257.0              NaN\n",
      "2010-12-09 20:00:00       254.0              NaN\n",
      "2010-12-09 21:00:00       246.0              NaN\n",
      "2010-12-09 22:00:00       241.0              NaN\n",
      "2010-12-09 23:00:00       230.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-12-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-12-10 00:00:00       218.0              NaN\n",
      "2010-12-10 01:00:00       215.0              NaN\n",
      "2010-12-10 02:00:00       212.0              NaN\n",
      "2010-12-10 03:00:00       231.0              NaN\n",
      "2010-12-10 04:00:00       237.0              NaN\n",
      "...                         ...              ...\n",
      "2010-12-16 19:00:00        72.0              NaN\n",
      "2010-12-16 20:00:00        82.0              NaN\n",
      "2010-12-16 21:00:00        98.0              NaN\n",
      "2010-12-16 22:00:00        95.0              NaN\n",
      "2010-12-16 23:00:00        55.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-12-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-12-17 00:00:00        41.0              NaN\n",
      "2010-12-17 01:00:00        39.0              NaN\n",
      "2010-12-17 02:00:00        36.0              NaN\n",
      "2010-12-17 03:00:00        34.0              NaN\n",
      "2010-12-17 04:00:00        34.0              NaN\n",
      "...                         ...              ...\n",
      "2010-12-23 19:00:00        46.0              NaN\n",
      "2010-12-23 20:00:00        52.0              NaN\n",
      "2010-12-23 21:00:00        32.0              NaN\n",
      "2010-12-23 22:00:00        42.0              NaN\n",
      "2010-12-23 23:00:00        32.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-12-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-12-24 00:00:00        35.0              NaN\n",
      "2010-12-24 01:00:00        26.0              NaN\n",
      "2010-12-24 02:00:00        27.0              NaN\n",
      "2010-12-24 03:00:00        28.0              NaN\n",
      "2010-12-24 04:00:00        19.0              NaN\n",
      "...                         ...              ...\n",
      "2010-12-30 19:00:00        23.0              NaN\n",
      "2010-12-30 20:00:00        21.0              NaN\n",
      "2010-12-30 21:00:00        16.0              NaN\n",
      "2010-12-30 22:00:00        20.0              NaN\n",
      "2010-12-30 23:00:00        20.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2010-12-31 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2010-12-31 00:00:00        21.0              NaN\n",
      "2010-12-31 01:00:00        20.0              NaN\n",
      "2010-12-31 02:00:00        16.0              NaN\n",
      "2010-12-31 03:00:00        16.0              NaN\n",
      "2010-12-31 04:00:00        13.0              NaN\n",
      "...                         ...              ...\n",
      "2011-01-06 19:00:00        27.0              NaN\n",
      "2011-01-06 20:00:00        21.0              NaN\n",
      "2011-01-06 21:00:00        28.0              NaN\n",
      "2011-01-06 22:00:00        22.0              NaN\n",
      "2011-01-06 23:00:00        18.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-01-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-01-07 00:00:00        17.0              NaN\n",
      "2011-01-07 01:00:00        16.0              NaN\n",
      "2011-01-07 02:00:00        27.0              NaN\n",
      "2011-01-07 03:00:00        17.0              NaN\n",
      "2011-01-07 04:00:00        13.0              NaN\n",
      "...                         ...              ...\n",
      "2011-01-13 19:00:00        95.0              NaN\n",
      "2011-01-13 20:00:00       120.0              NaN\n",
      "2011-01-13 21:00:00       125.0              NaN\n",
      "2011-01-13 22:00:00        95.0              NaN\n",
      "2011-01-13 23:00:00        27.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-01-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-01-14 00:00:00        25.0              NaN\n",
      "2011-01-14 01:00:00        25.0              NaN\n",
      "2011-01-14 02:00:00        18.0              NaN\n",
      "2011-01-14 03:00:00        12.0              NaN\n",
      "2011-01-14 04:00:00        17.0              NaN\n",
      "...                         ...              ...\n",
      "2011-01-20 19:00:00        74.0              NaN\n",
      "2011-01-20 20:00:00        82.0              NaN\n",
      "2011-01-20 21:00:00        91.0              NaN\n",
      "2011-01-20 22:00:00       100.0              NaN\n",
      "2011-01-20 23:00:00       124.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-01-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-01-21 00:00:00       120.0              NaN\n",
      "2011-01-21 01:00:00        45.0              NaN\n",
      "2011-01-21 02:00:00        36.0              NaN\n",
      "2011-01-21 03:00:00        34.0              NaN\n",
      "2011-01-21 04:00:00        39.0              NaN\n",
      "...                         ...              ...\n",
      "2011-01-27 19:00:00        26.0              NaN\n",
      "2011-01-27 20:00:00        19.0              NaN\n",
      "2011-01-27 21:00:00        20.0              NaN\n",
      "2011-01-27 22:00:00        32.0              NaN\n",
      "2011-01-27 23:00:00        32.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-01-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-01-28 00:00:00        18.0              NaN\n",
      "2011-01-28 01:00:00        13.0              NaN\n",
      "2011-01-28 02:00:00        23.0              NaN\n",
      "2011-01-28 03:00:00        23.0              NaN\n",
      "2011-01-28 04:00:00        20.0              NaN\n",
      "...                         ...              ...\n",
      "2011-02-03 19:00:00       167.0              NaN\n",
      "2011-02-03 20:00:00       250.0              NaN\n",
      "2011-02-03 21:00:00       288.0              NaN\n",
      "2011-02-03 22:00:00       289.0              NaN\n",
      "2011-02-03 23:00:00       299.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-02-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-02-04 00:00:00       378.0              NaN\n",
      "2011-02-04 01:00:00       270.0              NaN\n",
      "2011-02-04 02:00:00       263.0              NaN\n",
      "2011-02-04 03:00:00       234.0              NaN\n",
      "2011-02-04 04:00:00       171.0              NaN\n",
      "...                         ...              ...\n",
      "2011-02-10 19:00:00        95.0              NaN\n",
      "2011-02-10 20:00:00        98.0              NaN\n",
      "2011-02-10 21:00:00        98.0              NaN\n",
      "2011-02-10 22:00:00        97.0              NaN\n",
      "2011-02-10 23:00:00       107.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-02-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-02-11 00:00:00       100.0              NaN\n",
      "2011-02-11 01:00:00       105.0              NaN\n",
      "2011-02-11 02:00:00       103.0              NaN\n",
      "2011-02-11 03:00:00        96.0              NaN\n",
      "2011-02-11 04:00:00       104.0              NaN\n",
      "...                         ...              ...\n",
      "2011-02-17 19:00:00       188.0              NaN\n",
      "2011-02-17 20:00:00       288.0              NaN\n",
      "2011-02-17 21:00:00       404.0              NaN\n",
      "2011-02-17 22:00:00       422.0              NaN\n",
      "2011-02-17 23:00:00       411.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-02-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-02-18 00:00:00       345.0              NaN\n",
      "2011-02-18 01:00:00       353.0              NaN\n",
      "2011-02-18 02:00:00       399.0              NaN\n",
      "2011-02-18 03:00:00       384.0              NaN\n",
      "2011-02-18 04:00:00       282.0              NaN\n",
      "...                         ...              ...\n",
      "2011-02-24 19:00:00        52.0              NaN\n",
      "2011-02-24 20:00:00       142.0              NaN\n",
      "2011-02-24 21:00:00        99.0              NaN\n",
      "2011-02-24 22:00:00        55.0              NaN\n",
      "2011-02-24 23:00:00        55.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-02-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-02-25 00:00:00        53.0              NaN\n",
      "2011-02-25 01:00:00        67.0              NaN\n",
      "2011-02-25 02:00:00        72.0              NaN\n",
      "2011-02-25 03:00:00        77.0              NaN\n",
      "2011-02-25 04:00:00        75.0              NaN\n",
      "...                         ...              ...\n",
      "2011-03-03 19:00:00        11.0              NaN\n",
      "2011-03-03 20:00:00        37.0              NaN\n",
      "2011-03-03 21:00:00        36.0              NaN\n",
      "2011-03-03 22:00:00        40.0              NaN\n",
      "2011-03-03 23:00:00        43.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-03-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-03-04 00:00:00        48.0              NaN\n",
      "2011-03-04 01:00:00        46.0              NaN\n",
      "2011-03-04 02:00:00        45.0              NaN\n",
      "2011-03-04 03:00:00        46.0              NaN\n",
      "2011-03-04 04:00:00        52.0              NaN\n",
      "...                         ...              ...\n",
      "2011-03-10 19:00:00        25.0              NaN\n",
      "2011-03-10 20:00:00        38.0              NaN\n",
      "2011-03-10 21:00:00        43.0              NaN\n",
      "2011-03-10 22:00:00        48.0              NaN\n",
      "2011-03-10 23:00:00        48.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-03-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-03-11 00:00:00        47.0              NaN\n",
      "2011-03-11 01:00:00        40.0              NaN\n",
      "2011-03-11 02:00:00        40.0              NaN\n",
      "2011-03-11 03:00:00        44.0              NaN\n",
      "2011-03-11 04:00:00        26.0              NaN\n",
      "...                         ...              ...\n",
      "2011-03-17 19:00:00       126.0              NaN\n",
      "2011-03-17 20:00:00         NaN              NaN\n",
      "2011-03-17 21:00:00         NaN              NaN\n",
      "2011-03-17 22:00:00         NaN              NaN\n",
      "2011-03-17 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-03-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-03-18 00:00:00         NaN              NaN\n",
      "2011-03-18 01:00:00         NaN              NaN\n",
      "2011-03-18 02:00:00         NaN              NaN\n",
      "2011-03-18 03:00:00         NaN              NaN\n",
      "2011-03-18 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2011-03-24 19:00:00         6.0              NaN\n",
      "2011-03-24 20:00:00         4.0              NaN\n",
      "2011-03-24 21:00:00         3.0              NaN\n",
      "2011-03-24 22:00:00         4.0              NaN\n",
      "2011-03-24 23:00:00         8.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-03-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-03-25 00:00:00        13.0              NaN\n",
      "2011-03-25 01:00:00        13.0              NaN\n",
      "2011-03-25 02:00:00        15.0              NaN\n",
      "2011-03-25 03:00:00        23.0              NaN\n",
      "2011-03-25 04:00:00        23.0              NaN\n",
      "...                         ...              ...\n",
      "2011-03-31 19:00:00         NaN              NaN\n",
      "2011-03-31 20:00:00       295.0              NaN\n",
      "2011-03-31 21:00:00       328.0              NaN\n",
      "2011-03-31 22:00:00       287.0              NaN\n",
      "2011-03-31 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-04-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-04-01 00:00:00       222.0              NaN\n",
      "2011-04-01 01:00:00       147.0              NaN\n",
      "2011-04-01 02:00:00        51.0              NaN\n",
      "2011-04-01 03:00:00        23.0              NaN\n",
      "2011-04-01 04:00:00        19.0              NaN\n",
      "...                         ...              ...\n",
      "2011-04-07 19:00:00         NaN              NaN\n",
      "2011-04-07 20:00:00         NaN              NaN\n",
      "2011-04-07 21:00:00         NaN              NaN\n",
      "2011-04-07 22:00:00         NaN              NaN\n",
      "2011-04-07 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-04-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-04-08 00:00:00         NaN              NaN\n",
      "2011-04-08 01:00:00         NaN              NaN\n",
      "2011-04-08 02:00:00         NaN              NaN\n",
      "2011-04-08 03:00:00         NaN              NaN\n",
      "2011-04-08 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2011-04-14 19:00:00         NaN              NaN\n",
      "2011-04-14 20:00:00         NaN              NaN\n",
      "2011-04-14 21:00:00         NaN              NaN\n",
      "2011-04-14 22:00:00         NaN              NaN\n",
      "2011-04-14 23:00:00        53.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-04-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-04-15 00:00:00         NaN              NaN\n",
      "2011-04-15 01:00:00         NaN              NaN\n",
      "2011-04-15 02:00:00         NaN              NaN\n",
      "2011-04-15 03:00:00         NaN              NaN\n",
      "2011-04-15 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2011-04-21 19:00:00         NaN              NaN\n",
      "2011-04-21 20:00:00        49.0              NaN\n",
      "2011-04-21 21:00:00         NaN              NaN\n",
      "2011-04-21 22:00:00         NaN              NaN\n",
      "2011-04-21 23:00:00        74.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-04-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-04-22 00:00:00         NaN              NaN\n",
      "2011-04-22 01:00:00         NaN              NaN\n",
      "2011-04-22 02:00:00         NaN              NaN\n",
      "2011-04-22 03:00:00         NaN              NaN\n",
      "2011-04-22 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2011-04-28 19:00:00         NaN              NaN\n",
      "2011-04-28 20:00:00         NaN              NaN\n",
      "2011-04-28 21:00:00         NaN              NaN\n",
      "2011-04-28 22:00:00         NaN              NaN\n",
      "2011-04-28 23:00:00       165.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-04-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-04-29 00:00:00         NaN              NaN\n",
      "2011-04-29 01:00:00         NaN              NaN\n",
      "2011-04-29 02:00:00         NaN              NaN\n",
      "2011-04-29 03:00:00         NaN              NaN\n",
      "2011-04-29 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2011-05-05 19:00:00        63.0              NaN\n",
      "2011-05-05 20:00:00        95.0              NaN\n",
      "2011-05-05 21:00:00        79.0              NaN\n",
      "2011-05-05 22:00:00        75.0              NaN\n",
      "2011-05-05 23:00:00        97.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-05-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-05-06 00:00:00       110.0              NaN\n",
      "2011-05-06 01:00:00       111.0              NaN\n",
      "2011-05-06 02:00:00        98.0              NaN\n",
      "2011-05-06 03:00:00        97.0              NaN\n",
      "2011-05-06 04:00:00        86.0              NaN\n",
      "...                         ...              ...\n",
      "2011-05-12 19:00:00        32.0              NaN\n",
      "2011-05-12 20:00:00        24.0              NaN\n",
      "2011-05-12 21:00:00        16.0              NaN\n",
      "2011-05-12 22:00:00        29.0              NaN\n",
      "2011-05-12 23:00:00        27.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-05-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-05-13 00:00:00        22.0              NaN\n",
      "2011-05-13 01:00:00        20.0              NaN\n",
      "2011-05-13 02:00:00        18.0              NaN\n",
      "2011-05-13 03:00:00        24.0              NaN\n",
      "2011-05-13 04:00:00        18.0              NaN\n",
      "...                         ...              ...\n",
      "2011-05-19 19:00:00        20.0              NaN\n",
      "2011-05-19 20:00:00        29.0              NaN\n",
      "2011-05-19 21:00:00        27.0              NaN\n",
      "2011-05-19 22:00:00        27.0              NaN\n",
      "2011-05-19 23:00:00        28.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-05-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-05-20 00:00:00        33.0              NaN\n",
      "2011-05-20 01:00:00        35.0              NaN\n",
      "2011-05-20 02:00:00        26.0              NaN\n",
      "2011-05-20 03:00:00        20.0              NaN\n",
      "2011-05-20 04:00:00        23.0              NaN\n",
      "...                         ...              ...\n",
      "2011-05-26 19:00:00         NaN              NaN\n",
      "2011-05-26 20:00:00         NaN              NaN\n",
      "2011-05-26 21:00:00         NaN              NaN\n",
      "2011-05-26 22:00:00         NaN              NaN\n",
      "2011-05-26 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-05-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-05-27 00:00:00         NaN              NaN\n",
      "2011-05-27 01:00:00         NaN              NaN\n",
      "2011-05-27 02:00:00         NaN              NaN\n",
      "2011-05-27 03:00:00         NaN              NaN\n",
      "2011-05-27 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2011-06-02 19:00:00       116.0              NaN\n",
      "2011-06-02 20:00:00        74.0              NaN\n",
      "2011-06-02 21:00:00        64.0              NaN\n",
      "2011-06-02 22:00:00        73.0              NaN\n",
      "2011-06-02 23:00:00        69.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-06-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-06-03 00:00:00        68.0              NaN\n",
      "2011-06-03 01:00:00        37.0              NaN\n",
      "2011-06-03 02:00:00        37.0              NaN\n",
      "2011-06-03 03:00:00        32.0              NaN\n",
      "2011-06-03 04:00:00        23.0              NaN\n",
      "...                         ...              ...\n",
      "2011-06-09 19:00:00        75.0              NaN\n",
      "2011-06-09 20:00:00        96.0              NaN\n",
      "2011-06-09 21:00:00       123.0              NaN\n",
      "2011-06-09 22:00:00       119.0              NaN\n",
      "2011-06-09 23:00:00       121.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-06-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-06-10 00:00:00       121.0              NaN\n",
      "2011-06-10 01:00:00       121.0              NaN\n",
      "2011-06-10 02:00:00       108.0              NaN\n",
      "2011-06-10 03:00:00       117.0              NaN\n",
      "2011-06-10 04:00:00       176.0              NaN\n",
      "...                         ...              ...\n",
      "2011-06-16 19:00:00       137.0              NaN\n",
      "2011-06-16 20:00:00       113.0              NaN\n",
      "2011-06-16 21:00:00       117.0              NaN\n",
      "2011-06-16 22:00:00       114.0              NaN\n",
      "2011-06-16 23:00:00       115.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-06-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-06-17 00:00:00       134.0              NaN\n",
      "2011-06-17 01:00:00       134.0              NaN\n",
      "2011-06-17 02:00:00       125.0              NaN\n",
      "2011-06-17 03:00:00       106.0              NaN\n",
      "2011-06-17 04:00:00       121.0              NaN\n",
      "...                         ...              ...\n",
      "2011-06-23 19:00:00        44.0              NaN\n",
      "2011-06-23 20:00:00        34.0              NaN\n",
      "2011-06-23 21:00:00        36.0              NaN\n",
      "2011-06-23 22:00:00        29.0              NaN\n",
      "2011-06-23 23:00:00        49.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-06-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-06-24 00:00:00        54.0              NaN\n",
      "2011-06-24 01:00:00        22.0              NaN\n",
      "2011-06-24 02:00:00        25.0              NaN\n",
      "2011-06-24 03:00:00        11.0              NaN\n",
      "2011-06-24 04:00:00        25.0              NaN\n",
      "...                         ...              ...\n",
      "2011-06-30 19:00:00       213.0              NaN\n",
      "2011-06-30 20:00:00       213.0              NaN\n",
      "2011-06-30 21:00:00       218.0              NaN\n",
      "2011-06-30 22:00:00       233.0              NaN\n",
      "2011-06-30 23:00:00       245.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-07-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-07-01 00:00:00       254.0              NaN\n",
      "2011-07-01 01:00:00       232.0              NaN\n",
      "2011-07-01 02:00:00       220.0              NaN\n",
      "2011-07-01 03:00:00       222.0              NaN\n",
      "2011-07-01 04:00:00       230.0              NaN\n",
      "...                         ...              ...\n",
      "2011-07-07 19:00:00        22.0              NaN\n",
      "2011-07-07 20:00:00        19.0              NaN\n",
      "2011-07-07 21:00:00        23.0              NaN\n",
      "2011-07-07 22:00:00        28.0              NaN\n",
      "2011-07-07 23:00:00        21.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-07-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-07-08 00:00:00        22.0              NaN\n",
      "2011-07-08 01:00:00        31.0              NaN\n",
      "2011-07-08 02:00:00        37.0              NaN\n",
      "2011-07-08 03:00:00        38.0              NaN\n",
      "2011-07-08 04:00:00        32.0              NaN\n",
      "...                         ...              ...\n",
      "2011-07-14 19:00:00        94.0              NaN\n",
      "2011-07-14 20:00:00        83.0              NaN\n",
      "2011-07-14 21:00:00        71.0              NaN\n",
      "2011-07-14 22:00:00        62.0              NaN\n",
      "2011-07-14 23:00:00        44.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-07-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-07-15 00:00:00        39.0              NaN\n",
      "2011-07-15 01:00:00        42.0              NaN\n",
      "2011-07-15 02:00:00        58.0              NaN\n",
      "2011-07-15 03:00:00        59.0              NaN\n",
      "2011-07-15 04:00:00        57.0              NaN\n",
      "...                         ...              ...\n",
      "2011-07-21 19:00:00        75.0              NaN\n",
      "2011-07-21 20:00:00        91.0              NaN\n",
      "2011-07-21 21:00:00        91.0              NaN\n",
      "2011-07-21 22:00:00       106.0              NaN\n",
      "2011-07-21 23:00:00        94.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-07-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-07-22 00:00:00       118.0              NaN\n",
      "2011-07-22 01:00:00       129.0              NaN\n",
      "2011-07-22 02:00:00       116.0              NaN\n",
      "2011-07-22 03:00:00       119.0              NaN\n",
      "2011-07-22 04:00:00       118.0              NaN\n",
      "...                         ...              ...\n",
      "2011-07-28 19:00:00       209.0              NaN\n",
      "2011-07-28 20:00:00       238.0              NaN\n",
      "2011-07-28 21:00:00       259.0              NaN\n",
      "2011-07-28 22:00:00       269.0              NaN\n",
      "2011-07-28 23:00:00       286.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-07-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-07-29 00:00:00       311.0              NaN\n",
      "2011-07-29 01:00:00       306.0              NaN\n",
      "2011-07-29 02:00:00       307.0              NaN\n",
      "2011-07-29 03:00:00       313.0              NaN\n",
      "2011-07-29 04:00:00       300.0              NaN\n",
      "...                         ...              ...\n",
      "2011-08-04 19:00:00       117.0              NaN\n",
      "2011-08-04 20:00:00         NaN              NaN\n",
      "2011-08-04 21:00:00       101.0              NaN\n",
      "2011-08-04 22:00:00       107.0              NaN\n",
      "2011-08-04 23:00:00       118.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-08-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-08-05 00:00:00       138.0              NaN\n",
      "2011-08-05 01:00:00       166.0              NaN\n",
      "2011-08-05 02:00:00       144.0              NaN\n",
      "2011-08-05 03:00:00       146.0              NaN\n",
      "2011-08-05 04:00:00       154.0              NaN\n",
      "...                         ...              ...\n",
      "2011-08-11 19:00:00       113.0              NaN\n",
      "2011-08-11 20:00:00       130.0              NaN\n",
      "2011-08-11 21:00:00       133.0              NaN\n",
      "2011-08-11 22:00:00       161.0              NaN\n",
      "2011-08-11 23:00:00       123.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-08-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-08-12 00:00:00       115.0              NaN\n",
      "2011-08-12 01:00:00       111.0              NaN\n",
      "2011-08-12 02:00:00       105.0              NaN\n",
      "2011-08-12 03:00:00       111.0              NaN\n",
      "2011-08-12 04:00:00       103.0              NaN\n",
      "...                         ...              ...\n",
      "2011-08-18 19:00:00        37.0              NaN\n",
      "2011-08-18 20:00:00        49.0              NaN\n",
      "2011-08-18 21:00:00        44.0              NaN\n",
      "2011-08-18 22:00:00        47.0              NaN\n",
      "2011-08-18 23:00:00        48.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-08-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-08-19 00:00:00        55.0              NaN\n",
      "2011-08-19 01:00:00        61.0              NaN\n",
      "2011-08-19 02:00:00        56.0              NaN\n",
      "2011-08-19 03:00:00        51.0              NaN\n",
      "2011-08-19 04:00:00        62.0              NaN\n",
      "...                         ...              ...\n",
      "2011-08-25 19:00:00        96.0              NaN\n",
      "2011-08-25 20:00:00        92.0              NaN\n",
      "2011-08-25 21:00:00       109.0              NaN\n",
      "2011-08-25 22:00:00       118.0              NaN\n",
      "2011-08-25 23:00:00       121.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-08-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-08-26 00:00:00       108.0              NaN\n",
      "2011-08-26 01:00:00       121.0              NaN\n",
      "2011-08-26 02:00:00       126.0              NaN\n",
      "2011-08-26 03:00:00       114.0              NaN\n",
      "2011-08-26 04:00:00       116.0              NaN\n",
      "...                         ...              ...\n",
      "2011-09-01 19:00:00        31.0              NaN\n",
      "2011-09-01 20:00:00        32.0              NaN\n",
      "2011-09-01 21:00:00        32.0              NaN\n",
      "2011-09-01 22:00:00        31.0              NaN\n",
      "2011-09-01 23:00:00        39.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-09-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-09-02 00:00:00        32.0              NaN\n",
      "2011-09-02 01:00:00        40.0              NaN\n",
      "2011-09-02 02:00:00        36.0              NaN\n",
      "2011-09-02 03:00:00        43.0              NaN\n",
      "2011-09-02 04:00:00        58.0              NaN\n",
      "...                         ...              ...\n",
      "2011-09-08 19:00:00        58.0              NaN\n",
      "2011-09-08 20:00:00        44.0              NaN\n",
      "2011-09-08 21:00:00        43.0              NaN\n",
      "2011-09-08 22:00:00        48.0              NaN\n",
      "2011-09-08 23:00:00        43.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-09-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-09-09 00:00:00        58.0              NaN\n",
      "2011-09-09 01:00:00        54.0              NaN\n",
      "2011-09-09 02:00:00        48.0              NaN\n",
      "2011-09-09 03:00:00        34.0              NaN\n",
      "2011-09-09 04:00:00        26.0              NaN\n",
      "...                         ...              ...\n",
      "2011-09-15 19:00:00       124.0              NaN\n",
      "2011-09-15 20:00:00       108.0              NaN\n",
      "2011-09-15 21:00:00        70.0              NaN\n",
      "2011-09-15 22:00:00        92.0              NaN\n",
      "2011-09-15 23:00:00        36.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-09-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-09-16 00:00:00        31.0              NaN\n",
      "2011-09-16 01:00:00        24.0              NaN\n",
      "2011-09-16 02:00:00        17.0              NaN\n",
      "2011-09-16 03:00:00        25.0              NaN\n",
      "2011-09-16 04:00:00        22.0              NaN\n",
      "...                         ...              ...\n",
      "2011-09-22 19:00:00        74.0              NaN\n",
      "2011-09-22 20:00:00        60.0              NaN\n",
      "2011-09-22 21:00:00       133.0              NaN\n",
      "2011-09-22 22:00:00       125.0              NaN\n",
      "2011-09-22 23:00:00       164.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-09-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-09-23 00:00:00       140.0              NaN\n",
      "2011-09-23 01:00:00       138.0              NaN\n",
      "2011-09-23 02:00:00       137.0              NaN\n",
      "2011-09-23 03:00:00       114.0              NaN\n",
      "2011-09-23 04:00:00        58.0              NaN\n",
      "...                         ...              ...\n",
      "2011-09-29 19:00:00        24.0              NaN\n",
      "2011-09-29 20:00:00        33.0              NaN\n",
      "2011-09-29 21:00:00        23.0              NaN\n",
      "2011-09-29 22:00:00        27.0              NaN\n",
      "2011-09-29 23:00:00        19.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-09-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-09-30 00:00:00        20.0              NaN\n",
      "2011-09-30 01:00:00        19.0              NaN\n",
      "2011-09-30 02:00:00        11.0              NaN\n",
      "2011-09-30 03:00:00        26.0              NaN\n",
      "2011-09-30 04:00:00        20.0              NaN\n",
      "...                         ...              ...\n",
      "2011-10-06 19:00:00         NaN              NaN\n",
      "2011-10-06 20:00:00         NaN              NaN\n",
      "2011-10-06 21:00:00         NaN              NaN\n",
      "2011-10-06 22:00:00         NaN              NaN\n",
      "2011-10-06 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-10-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-10-07 00:00:00         NaN              NaN\n",
      "2011-10-07 01:00:00         NaN              NaN\n",
      "2011-10-07 02:00:00         NaN              NaN\n",
      "2011-10-07 03:00:00         NaN              NaN\n",
      "2011-10-07 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2011-10-13 19:00:00        46.0              NaN\n",
      "2011-10-13 20:00:00        57.0              NaN\n",
      "2011-10-13 21:00:00        30.0              NaN\n",
      "2011-10-13 22:00:00        39.0              NaN\n",
      "2011-10-13 23:00:00        23.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-10-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-10-14 00:00:00        23.0              NaN\n",
      "2011-10-14 01:00:00        32.0              NaN\n",
      "2011-10-14 02:00:00        21.0              NaN\n",
      "2011-10-14 03:00:00        24.0              NaN\n",
      "2011-10-14 04:00:00        15.0              NaN\n",
      "...                         ...              ...\n",
      "2011-10-20 19:00:00       299.0              NaN\n",
      "2011-10-20 20:00:00       300.0              NaN\n",
      "2011-10-20 21:00:00       308.0              NaN\n",
      "2011-10-20 22:00:00       302.0              NaN\n",
      "2011-10-20 23:00:00       298.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-10-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-10-21 00:00:00       266.0              NaN\n",
      "2011-10-21 01:00:00       285.0              NaN\n",
      "2011-10-21 02:00:00       282.0              NaN\n",
      "2011-10-21 03:00:00       273.0              NaN\n",
      "2011-10-21 04:00:00       275.0              NaN\n",
      "...                         ...              ...\n",
      "2011-10-27 19:00:00       208.0              NaN\n",
      "2011-10-27 20:00:00       224.0              NaN\n",
      "2011-10-27 21:00:00       235.0              NaN\n",
      "2011-10-27 22:00:00       273.0              NaN\n",
      "2011-10-27 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-10-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-10-28 00:00:00       261.0              NaN\n",
      "2011-10-28 01:00:00       242.0              NaN\n",
      "2011-10-28 02:00:00       186.0              NaN\n",
      "2011-10-28 03:00:00       187.0              NaN\n",
      "2011-10-28 04:00:00       180.0              NaN\n",
      "...                         ...              ...\n",
      "2011-11-03 19:00:00       100.0              NaN\n",
      "2011-11-03 20:00:00        94.0              NaN\n",
      "2011-11-03 21:00:00       108.0              NaN\n",
      "2011-11-03 22:00:00       123.0              NaN\n",
      "2011-11-03 23:00:00       102.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-11-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-11-04 00:00:00        89.0              NaN\n",
      "2011-11-04 01:00:00        78.0              NaN\n",
      "2011-11-04 02:00:00        76.0              NaN\n",
      "2011-11-04 03:00:00       108.0              NaN\n",
      "2011-11-04 04:00:00       107.0              NaN\n",
      "...                         ...              ...\n",
      "2011-11-10 19:00:00        84.0              NaN\n",
      "2011-11-10 20:00:00        92.0              NaN\n",
      "2011-11-10 21:00:00       113.0              NaN\n",
      "2011-11-10 22:00:00       139.0              NaN\n",
      "2011-11-10 23:00:00       135.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-11-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-11-11 00:00:00       161.0              NaN\n",
      "2011-11-11 01:00:00       145.0              NaN\n",
      "2011-11-11 02:00:00       154.0              NaN\n",
      "2011-11-11 03:00:00       139.0              NaN\n",
      "2011-11-11 04:00:00       153.0              NaN\n",
      "...                         ...              ...\n",
      "2011-11-17 19:00:00       166.0              NaN\n",
      "2011-11-17 20:00:00       144.0              NaN\n",
      "2011-11-17 21:00:00       136.0              NaN\n",
      "2011-11-17 22:00:00       128.0              NaN\n",
      "2011-11-17 23:00:00       139.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-11-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-11-18 00:00:00       141.0              NaN\n",
      "2011-11-18 01:00:00       157.0              NaN\n",
      "2011-11-18 02:00:00       142.0              NaN\n",
      "2011-11-18 03:00:00       127.0              NaN\n",
      "2011-11-18 04:00:00        95.0              NaN\n",
      "...                         ...              ...\n",
      "2011-11-24 19:00:00       282.0              NaN\n",
      "2011-11-24 20:00:00       290.0              NaN\n",
      "2011-11-24 21:00:00       323.0              NaN\n",
      "2011-11-24 22:00:00       333.0              NaN\n",
      "2011-11-24 23:00:00       305.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-11-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-11-25 00:00:00       280.0              NaN\n",
      "2011-11-25 01:00:00       260.0              NaN\n",
      "2011-11-25 02:00:00       207.0              NaN\n",
      "2011-11-25 03:00:00       232.0              NaN\n",
      "2011-11-25 04:00:00       155.0              NaN\n",
      "...                         ...              ...\n",
      "2011-12-01 19:00:00       209.0              NaN\n",
      "2011-12-01 20:00:00       187.0              NaN\n",
      "2011-12-01 21:00:00       198.0              NaN\n",
      "2011-12-01 22:00:00       219.0              NaN\n",
      "2011-12-01 23:00:00       243.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-12-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-12-02 00:00:00       287.0              NaN\n",
      "2011-12-02 01:00:00       303.0              NaN\n",
      "2011-12-02 02:00:00       282.0              NaN\n",
      "2011-12-02 03:00:00       284.0              NaN\n",
      "2011-12-02 04:00:00       290.0              NaN\n",
      "...                         ...              ...\n",
      "2011-12-08 19:00:00         9.0              NaN\n",
      "2011-12-08 20:00:00         7.0              NaN\n",
      "2011-12-08 21:00:00         7.0              NaN\n",
      "2011-12-08 22:00:00         7.0              NaN\n",
      "2011-12-08 23:00:00         7.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-12-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-12-09 00:00:00        17.0              NaN\n",
      "2011-12-09 01:00:00        17.0              NaN\n",
      "2011-12-09 02:00:00        18.0              NaN\n",
      "2011-12-09 03:00:00        13.0              NaN\n",
      "2011-12-09 04:00:00        13.0              NaN\n",
      "...                         ...              ...\n",
      "2011-12-15 19:00:00         9.0              NaN\n",
      "2011-12-15 20:00:00        10.0              NaN\n",
      "2011-12-15 21:00:00        11.0              NaN\n",
      "2011-12-15 22:00:00        10.0              NaN\n",
      "2011-12-15 23:00:00        15.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-12-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-12-16 00:00:00        11.0              NaN\n",
      "2011-12-16 01:00:00        10.0              NaN\n",
      "2011-12-16 02:00:00         9.0              NaN\n",
      "2011-12-16 03:00:00         8.0              NaN\n",
      "2011-12-16 04:00:00         8.0              NaN\n",
      "...                         ...              ...\n",
      "2011-12-22 19:00:00        51.0              NaN\n",
      "2011-12-22 20:00:00        64.0              NaN\n",
      "2011-12-22 21:00:00        68.0              NaN\n",
      "2011-12-22 22:00:00        70.0              NaN\n",
      "2011-12-22 23:00:00        95.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-12-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-12-23 00:00:00       118.0              NaN\n",
      "2011-12-23 01:00:00       117.0              NaN\n",
      "2011-12-23 02:00:00       119.0              NaN\n",
      "2011-12-23 03:00:00       106.0              NaN\n",
      "2011-12-23 04:00:00        98.0              NaN\n",
      "...                         ...              ...\n",
      "2011-12-29 19:00:00        91.0              NaN\n",
      "2011-12-29 20:00:00        79.0              NaN\n",
      "2011-12-29 21:00:00        64.0              NaN\n",
      "2011-12-29 22:00:00        69.0              NaN\n",
      "2011-12-29 23:00:00        71.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2011-12-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2011-12-30 00:00:00        67.0              NaN\n",
      "2011-12-30 01:00:00        64.0              NaN\n",
      "2011-12-30 02:00:00        64.0              NaN\n",
      "2011-12-30 03:00:00        70.0              NaN\n",
      "2011-12-30 04:00:00        71.0              NaN\n",
      "...                         ...              ...\n",
      "2012-01-05 19:00:00       235.0              NaN\n",
      "2012-01-05 20:00:00       241.0              NaN\n",
      "2012-01-05 21:00:00       189.0              NaN\n",
      "2012-01-05 22:00:00       161.0              NaN\n",
      "2012-01-05 23:00:00       189.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-01-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-01-06 00:00:00       161.0              NaN\n",
      "2012-01-06 01:00:00       161.0              NaN\n",
      "2012-01-06 02:00:00       159.0              NaN\n",
      "2012-01-06 03:00:00       197.0              NaN\n",
      "2012-01-06 04:00:00       183.0              NaN\n",
      "...                         ...              ...\n",
      "2012-01-12 19:00:00       171.0              NaN\n",
      "2012-01-12 20:00:00       156.0              NaN\n",
      "2012-01-12 21:00:00       230.0              NaN\n",
      "2012-01-12 22:00:00       182.0              NaN\n",
      "2012-01-12 23:00:00       100.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-01-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-01-13 00:00:00        60.0              NaN\n",
      "2012-01-13 01:00:00        50.0              NaN\n",
      "2012-01-13 02:00:00        52.0              NaN\n",
      "2012-01-13 03:00:00        57.0              NaN\n",
      "2012-01-13 04:00:00        41.0              NaN\n",
      "...                         ...              ...\n",
      "2012-01-19 19:00:00       436.0              NaN\n",
      "2012-01-19 20:00:00       459.0              NaN\n",
      "2012-01-19 21:00:00       494.0              NaN\n",
      "2012-01-19 22:00:00       522.0              NaN\n",
      "2012-01-19 23:00:00       479.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-01-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-01-20 00:00:00       315.0              NaN\n",
      "2012-01-20 01:00:00       121.0              NaN\n",
      "2012-01-20 02:00:00        49.0              NaN\n",
      "2012-01-20 03:00:00        52.0              NaN\n",
      "2012-01-20 04:00:00        47.0              NaN\n",
      "...                         ...              ...\n",
      "2012-01-26 19:00:00       125.0              NaN\n",
      "2012-01-26 20:00:00       158.0              NaN\n",
      "2012-01-26 21:00:00       227.0              NaN\n",
      "2012-01-26 22:00:00       192.0              NaN\n",
      "2012-01-26 23:00:00       113.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-01-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-01-27 00:00:00        51.0              NaN\n",
      "2012-01-27 01:00:00        25.0              NaN\n",
      "2012-01-27 02:00:00        18.0              NaN\n",
      "2012-01-27 03:00:00        40.0              NaN\n",
      "2012-01-27 04:00:00       139.0              NaN\n",
      "...                         ...              ...\n",
      "2012-02-02 19:00:00        69.0              NaN\n",
      "2012-02-02 20:00:00        75.0              NaN\n",
      "2012-02-02 21:00:00        82.0              NaN\n",
      "2012-02-02 22:00:00        92.0              NaN\n",
      "2012-02-02 23:00:00        64.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-02-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-02-03 00:00:00        61.0              NaN\n",
      "2012-02-03 01:00:00        40.0              NaN\n",
      "2012-02-03 02:00:00        60.0              NaN\n",
      "2012-02-03 03:00:00        68.0              NaN\n",
      "2012-02-03 04:00:00        68.0              NaN\n",
      "...                         ...              ...\n",
      "2012-02-09 19:00:00        64.0              NaN\n",
      "2012-02-09 20:00:00        56.0              NaN\n",
      "2012-02-09 21:00:00        57.0              NaN\n",
      "2012-02-09 22:00:00        21.0              NaN\n",
      "2012-02-09 23:00:00         6.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-02-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-02-10 00:00:00         4.0              NaN\n",
      "2012-02-10 01:00:00         6.0              NaN\n",
      "2012-02-10 02:00:00         8.0              NaN\n",
      "2012-02-10 03:00:00         7.0              NaN\n",
      "2012-02-10 04:00:00         9.0              NaN\n",
      "...                         ...              ...\n",
      "2012-02-16 19:00:00        13.0              NaN\n",
      "2012-02-16 20:00:00        15.0              NaN\n",
      "2012-02-16 21:00:00        15.0              NaN\n",
      "2012-02-16 22:00:00        20.0              NaN\n",
      "2012-02-16 23:00:00         7.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-02-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-02-17 00:00:00         8.0              NaN\n",
      "2012-02-17 01:00:00         9.0              NaN\n",
      "2012-02-17 02:00:00        11.0              NaN\n",
      "2012-02-17 03:00:00        12.0              NaN\n",
      "2012-02-17 04:00:00        11.0              NaN\n",
      "...                         ...              ...\n",
      "2012-02-23 19:00:00        33.0              NaN\n",
      "2012-02-23 20:00:00        57.0              NaN\n",
      "2012-02-23 21:00:00        70.0              NaN\n",
      "2012-02-23 22:00:00        81.0              NaN\n",
      "2012-02-23 23:00:00        84.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-02-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-02-24 00:00:00        78.0              NaN\n",
      "2012-02-24 01:00:00        74.0              NaN\n",
      "2012-02-24 02:00:00        79.0              NaN\n",
      "2012-02-24 03:00:00        99.0              NaN\n",
      "2012-02-24 04:00:00        99.0              NaN\n",
      "...                         ...              ...\n",
      "2012-03-01 19:00:00       213.0              NaN\n",
      "2012-03-01 20:00:00       235.0              NaN\n",
      "2012-03-01 21:00:00       267.0              NaN\n",
      "2012-03-01 22:00:00       278.0              NaN\n",
      "2012-03-01 23:00:00       269.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-03-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-03-02 00:00:00       165.0              NaN\n",
      "2012-03-02 01:00:00       154.0              NaN\n",
      "2012-03-02 02:00:00       112.0              NaN\n",
      "2012-03-02 03:00:00       126.0              NaN\n",
      "2012-03-02 04:00:00       113.0              NaN\n",
      "...                         ...              ...\n",
      "2012-03-08 19:00:00        18.0              NaN\n",
      "2012-03-08 20:00:00        31.0              NaN\n",
      "2012-03-08 21:00:00        37.0              NaN\n",
      "2012-03-08 22:00:00        26.0              NaN\n",
      "2012-03-08 23:00:00        18.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-03-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-03-09 00:00:00        73.0              NaN\n",
      "2012-03-09 01:00:00       102.0              NaN\n",
      "2012-03-09 02:00:00       112.0              NaN\n",
      "2012-03-09 03:00:00        93.0              NaN\n",
      "2012-03-09 04:00:00        89.0              NaN\n",
      "...                         ...              ...\n",
      "2012-03-15 19:00:00       165.0              NaN\n",
      "2012-03-15 20:00:00       170.0              NaN\n",
      "2012-03-15 21:00:00       183.0              NaN\n",
      "2012-03-15 22:00:00       176.0              NaN\n",
      "2012-03-15 23:00:00       180.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-03-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-03-16 00:00:00       187.0              NaN\n",
      "2012-03-16 01:00:00       180.0              NaN\n",
      "2012-03-16 02:00:00       181.0              NaN\n",
      "2012-03-16 03:00:00       183.0              NaN\n",
      "2012-03-16 04:00:00       186.0              NaN\n",
      "...                         ...              ...\n",
      "2012-03-22 19:00:00        71.0              NaN\n",
      "2012-03-22 20:00:00        57.0              NaN\n",
      "2012-03-22 21:00:00        50.0              NaN\n",
      "2012-03-22 22:00:00        39.0              NaN\n",
      "2012-03-22 23:00:00        37.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-03-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-03-23 00:00:00        41.0              NaN\n",
      "2012-03-23 01:00:00        37.0              NaN\n",
      "2012-03-23 02:00:00        40.0              NaN\n",
      "2012-03-23 03:00:00        50.0              NaN\n",
      "2012-03-23 04:00:00        54.0              NaN\n",
      "...                         ...              ...\n",
      "2012-03-29 19:00:00        10.0              NaN\n",
      "2012-03-29 20:00:00         8.0              NaN\n",
      "2012-03-29 21:00:00        10.0              NaN\n",
      "2012-03-29 22:00:00        18.0              NaN\n",
      "2012-03-29 23:00:00        15.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-03-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-03-30 00:00:00        10.0              NaN\n",
      "2012-03-30 01:00:00         7.0              NaN\n",
      "2012-03-30 02:00:00        10.0              NaN\n",
      "2012-03-30 03:00:00         7.0              NaN\n",
      "2012-03-30 04:00:00         5.0              NaN\n",
      "...                         ...              ...\n",
      "2012-04-05 19:00:00        12.0              NaN\n",
      "2012-04-05 20:00:00        13.0              NaN\n",
      "2012-04-05 21:00:00        27.0              NaN\n",
      "2012-04-05 22:00:00        15.0              NaN\n",
      "2012-04-05 23:00:00        15.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-04-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-04-06 00:00:00        20.0              NaN\n",
      "2012-04-06 01:00:00        20.0              NaN\n",
      "2012-04-06 02:00:00        17.0              NaN\n",
      "2012-04-06 03:00:00        24.0              NaN\n",
      "2012-04-06 04:00:00        36.0              NaN\n",
      "...                         ...              ...\n",
      "2012-04-12 19:00:00        19.0              NaN\n",
      "2012-04-12 20:00:00        29.0              NaN\n",
      "2012-04-12 21:00:00        28.0              NaN\n",
      "2012-04-12 22:00:00        33.0              NaN\n",
      "2012-04-12 23:00:00        51.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-04-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-04-13 00:00:00        56.0              NaN\n",
      "2012-04-13 01:00:00        84.0              NaN\n",
      "2012-04-13 02:00:00        80.0              NaN\n",
      "2012-04-13 03:00:00        77.0              NaN\n",
      "2012-04-13 04:00:00        89.0              NaN\n",
      "...                         ...              ...\n",
      "2012-04-19 19:00:00       148.0              NaN\n",
      "2012-04-19 20:00:00       151.0              NaN\n",
      "2012-04-19 21:00:00       157.0              NaN\n",
      "2012-04-19 22:00:00       159.0              NaN\n",
      "2012-04-19 23:00:00       164.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-04-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-04-20 00:00:00       170.0              NaN\n",
      "2012-04-20 01:00:00       174.0              NaN\n",
      "2012-04-20 02:00:00       169.0              NaN\n",
      "2012-04-20 03:00:00       183.0              NaN\n",
      "2012-04-20 04:00:00       176.0              NaN\n",
      "...                         ...              ...\n",
      "2012-04-26 19:00:00        78.0              NaN\n",
      "2012-04-26 20:00:00       144.0              NaN\n",
      "2012-04-26 21:00:00       163.0              NaN\n",
      "2012-04-26 22:00:00       176.0              NaN\n",
      "2012-04-26 23:00:00       143.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-04-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-04-27 00:00:00       152.0              NaN\n",
      "2012-04-27 01:00:00       160.0              NaN\n",
      "2012-04-27 02:00:00       148.0              NaN\n",
      "2012-04-27 03:00:00       134.0              NaN\n",
      "2012-04-27 04:00:00       112.0              NaN\n",
      "...                         ...              ...\n",
      "2012-05-03 19:00:00        19.0              NaN\n",
      "2012-05-03 20:00:00        49.0              NaN\n",
      "2012-05-03 21:00:00        91.0              NaN\n",
      "2012-05-03 22:00:00       101.0              NaN\n",
      "2012-05-03 23:00:00        99.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-05-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-05-04 00:00:00       106.0              NaN\n",
      "2012-05-04 01:00:00       123.0              NaN\n",
      "2012-05-04 02:00:00       128.0              NaN\n",
      "2012-05-04 03:00:00       132.0              NaN\n",
      "2012-05-04 04:00:00       128.0              NaN\n",
      "...                         ...              ...\n",
      "2012-05-10 19:00:00       152.0              NaN\n",
      "2012-05-10 20:00:00       204.0              NaN\n",
      "2012-05-10 21:00:00       209.0              NaN\n",
      "2012-05-10 22:00:00       228.0              NaN\n",
      "2012-05-10 23:00:00       247.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-05-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-05-11 00:00:00       265.0              NaN\n",
      "2012-05-11 01:00:00       263.0              NaN\n",
      "2012-05-11 02:00:00       207.0              NaN\n",
      "2012-05-11 03:00:00       153.0              NaN\n",
      "2012-05-11 04:00:00       152.0              NaN\n",
      "...                         ...              ...\n",
      "2012-05-17 19:00:00        25.0              NaN\n",
      "2012-05-17 20:00:00        27.0              NaN\n",
      "2012-05-17 21:00:00        27.0              NaN\n",
      "2012-05-17 22:00:00        27.0              NaN\n",
      "2012-05-17 23:00:00        34.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-05-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-05-18 00:00:00        32.0              NaN\n",
      "2012-05-18 01:00:00        28.0              NaN\n",
      "2012-05-18 02:00:00        35.0              NaN\n",
      "2012-05-18 03:00:00        45.0              NaN\n",
      "2012-05-18 04:00:00        52.0              NaN\n",
      "...                         ...              ...\n",
      "2012-05-24 19:00:00        16.0              NaN\n",
      "2012-05-24 20:00:00        19.0              NaN\n",
      "2012-05-24 21:00:00        31.0              NaN\n",
      "2012-05-24 22:00:00        56.0              NaN\n",
      "2012-05-24 23:00:00        69.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-05-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-05-25 00:00:00        91.0              NaN\n",
      "2012-05-25 01:00:00        84.0              NaN\n",
      "2012-05-25 02:00:00        70.0              NaN\n",
      "2012-05-25 03:00:00        48.0              NaN\n",
      "2012-05-25 04:00:00        48.0              NaN\n",
      "...                         ...              ...\n",
      "2012-05-31 19:00:00        43.0              NaN\n",
      "2012-05-31 20:00:00        48.0              NaN\n",
      "2012-05-31 21:00:00        50.0              NaN\n",
      "2012-05-31 22:00:00        40.0              NaN\n",
      "2012-05-31 23:00:00        50.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-06-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-06-01 00:00:00        51.0              NaN\n",
      "2012-06-01 01:00:00        55.0              NaN\n",
      "2012-06-01 02:00:00        63.0              NaN\n",
      "2012-06-01 03:00:00        57.0              NaN\n",
      "2012-06-01 04:00:00        58.0              NaN\n",
      "...                         ...              ...\n",
      "2012-06-07 19:00:00        80.0              NaN\n",
      "2012-06-07 20:00:00       108.0              NaN\n",
      "2012-06-07 21:00:00       129.0              NaN\n",
      "2012-06-07 22:00:00        29.0              NaN\n",
      "2012-06-07 23:00:00        17.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-06-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-06-08 00:00:00        23.0              NaN\n",
      "2012-06-08 01:00:00       101.0              NaN\n",
      "2012-06-08 02:00:00        99.0              NaN\n",
      "2012-06-08 03:00:00        85.0              NaN\n",
      "2012-06-08 04:00:00        90.0              NaN\n",
      "...                         ...              ...\n",
      "2012-06-14 19:00:00        10.0              NaN\n",
      "2012-06-14 20:00:00         8.0              NaN\n",
      "2012-06-14 21:00:00         7.0              NaN\n",
      "2012-06-14 22:00:00         7.0              NaN\n",
      "2012-06-14 23:00:00        11.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-06-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-06-15 00:00:00        11.0              NaN\n",
      "2012-06-15 01:00:00        21.0              NaN\n",
      "2012-06-15 02:00:00        15.0              NaN\n",
      "2012-06-15 03:00:00         9.0              NaN\n",
      "2012-06-15 04:00:00        14.0              NaN\n",
      "...                         ...              ...\n",
      "2012-06-21 19:00:00       183.0              NaN\n",
      "2012-06-21 20:00:00       191.0              NaN\n",
      "2012-06-21 21:00:00        60.0              NaN\n",
      "2012-06-21 22:00:00        52.0              NaN\n",
      "2012-06-21 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-06-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-06-22 00:00:00        65.0              NaN\n",
      "2012-06-22 01:00:00        71.0              NaN\n",
      "2012-06-22 02:00:00        83.0              NaN\n",
      "2012-06-22 03:00:00        84.0              NaN\n",
      "2012-06-22 04:00:00        88.0              NaN\n",
      "...                         ...              ...\n",
      "2012-06-28 19:00:00       178.0              NaN\n",
      "2012-06-28 20:00:00       174.0              NaN\n",
      "2012-06-28 21:00:00       175.0              NaN\n",
      "2012-06-28 22:00:00       151.0              NaN\n",
      "2012-06-28 23:00:00       129.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-06-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-06-29 00:00:00         NaN              NaN\n",
      "2012-06-29 01:00:00       109.0              NaN\n",
      "2012-06-29 02:00:00       104.0              NaN\n",
      "2012-06-29 03:00:00       118.0              NaN\n",
      "2012-06-29 04:00:00       105.0              NaN\n",
      "...                         ...              ...\n",
      "2012-07-05 19:00:00       133.0              NaN\n",
      "2012-07-05 20:00:00       124.0              NaN\n",
      "2012-07-05 21:00:00       122.0              NaN\n",
      "2012-07-05 22:00:00        32.0              NaN\n",
      "2012-07-05 23:00:00        34.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-07-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-07-06 00:00:00        46.0              NaN\n",
      "2012-07-06 01:00:00        47.0              NaN\n",
      "2012-07-06 02:00:00        53.0              NaN\n",
      "2012-07-06 03:00:00        55.0              NaN\n",
      "2012-07-06 04:00:00        52.0              NaN\n",
      "...                         ...              ...\n",
      "2012-07-12 19:00:00         NaN              NaN\n",
      "2012-07-12 20:00:00         NaN              NaN\n",
      "2012-07-12 21:00:00        17.0              NaN\n",
      "2012-07-12 22:00:00        16.0              NaN\n",
      "2012-07-12 23:00:00        29.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-07-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-07-13 00:00:00        26.0              NaN\n",
      "2012-07-13 01:00:00        18.0              NaN\n",
      "2012-07-13 02:00:00        17.0              NaN\n",
      "2012-07-13 03:00:00        31.0              NaN\n",
      "2012-07-13 04:00:00        25.0              NaN\n",
      "...                         ...              ...\n",
      "2012-07-19 19:00:00       124.0              NaN\n",
      "2012-07-19 20:00:00       138.0              NaN\n",
      "2012-07-19 21:00:00       132.0              NaN\n",
      "2012-07-19 22:00:00       146.0              NaN\n",
      "2012-07-19 23:00:00       160.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-07-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-07-20 00:00:00       173.0              NaN\n",
      "2012-07-20 01:00:00       167.0              NaN\n",
      "2012-07-20 02:00:00       179.0              NaN\n",
      "2012-07-20 03:00:00       187.0              NaN\n",
      "2012-07-20 04:00:00       195.0              NaN\n",
      "...                         ...              ...\n",
      "2012-07-26 19:00:00        48.0              NaN\n",
      "2012-07-26 20:00:00        39.0              NaN\n",
      "2012-07-26 21:00:00        51.0              NaN\n",
      "2012-07-26 22:00:00        56.0              NaN\n",
      "2012-07-26 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-07-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-07-27 00:00:00        63.0              NaN\n",
      "2012-07-27 01:00:00        53.0              NaN\n",
      "2012-07-27 02:00:00        59.0              NaN\n",
      "2012-07-27 03:00:00        54.0              NaN\n",
      "2012-07-27 04:00:00        82.0              NaN\n",
      "...                         ...              ...\n",
      "2012-08-02 19:00:00        30.0              NaN\n",
      "2012-08-02 20:00:00        33.0              NaN\n",
      "2012-08-02 21:00:00        33.0              NaN\n",
      "2012-08-02 22:00:00        55.0              NaN\n",
      "2012-08-02 23:00:00        69.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-08-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-08-03 00:00:00        77.0              NaN\n",
      "2012-08-03 01:00:00        64.0              NaN\n",
      "2012-08-03 02:00:00        61.0              NaN\n",
      "2012-08-03 03:00:00        50.0              NaN\n",
      "2012-08-03 04:00:00        48.0              NaN\n",
      "...                         ...              ...\n",
      "2012-08-09 19:00:00        38.0              NaN\n",
      "2012-08-09 20:00:00        38.0              NaN\n",
      "2012-08-09 21:00:00        34.0              NaN\n",
      "2012-08-09 22:00:00        41.0              NaN\n",
      "2012-08-09 23:00:00        37.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-08-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-08-10 00:00:00        43.0              NaN\n",
      "2012-08-10 01:00:00        53.0              NaN\n",
      "2012-08-10 02:00:00        57.0              NaN\n",
      "2012-08-10 03:00:00        72.0              NaN\n",
      "2012-08-10 04:00:00        85.0              NaN\n",
      "...                         ...              ...\n",
      "2012-08-16 19:00:00        64.0              NaN\n",
      "2012-08-16 20:00:00        53.0              NaN\n",
      "2012-08-16 21:00:00        73.0              NaN\n",
      "2012-08-16 22:00:00        77.0              NaN\n",
      "2012-08-16 23:00:00        70.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-08-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-08-17 00:00:00        67.0              NaN\n",
      "2012-08-17 01:00:00        64.0              NaN\n",
      "2012-08-17 02:00:00        77.0              NaN\n",
      "2012-08-17 03:00:00        78.0              NaN\n",
      "2012-08-17 04:00:00        96.0              NaN\n",
      "...                         ...              ...\n",
      "2012-08-23 19:00:00        65.0              NaN\n",
      "2012-08-23 20:00:00        66.0              NaN\n",
      "2012-08-23 21:00:00        62.0              NaN\n",
      "2012-08-23 22:00:00        63.0              NaN\n",
      "2012-08-23 23:00:00        66.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-08-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-08-24 00:00:00        71.0              NaN\n",
      "2012-08-24 01:00:00        73.0              NaN\n",
      "2012-08-24 02:00:00        81.0              NaN\n",
      "2012-08-24 03:00:00        88.0              NaN\n",
      "2012-08-24 04:00:00       101.0              NaN\n",
      "...                         ...              ...\n",
      "2012-08-30 19:00:00       224.0              NaN\n",
      "2012-08-30 20:00:00       229.0              NaN\n",
      "2012-08-30 21:00:00       215.0              NaN\n",
      "2012-08-30 22:00:00       204.0              NaN\n",
      "2012-08-30 23:00:00       194.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-08-31 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-08-31 00:00:00       188.0              NaN\n",
      "2012-08-31 01:00:00       197.0              NaN\n",
      "2012-08-31 02:00:00       206.0              NaN\n",
      "2012-08-31 03:00:00       207.0              NaN\n",
      "2012-08-31 04:00:00       212.0              NaN\n",
      "...                         ...              ...\n",
      "2012-09-06 19:00:00       150.0              NaN\n",
      "2012-09-06 20:00:00       154.0              NaN\n",
      "2012-09-06 21:00:00       152.0              NaN\n",
      "2012-09-06 22:00:00       144.0              NaN\n",
      "2012-09-06 23:00:00       143.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-09-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-09-07 00:00:00       141.0              NaN\n",
      "2012-09-07 01:00:00       151.0              NaN\n",
      "2012-09-07 02:00:00       140.0              NaN\n",
      "2012-09-07 03:00:00       139.0              NaN\n",
      "2012-09-07 04:00:00       135.0              NaN\n",
      "...                         ...              ...\n",
      "2012-09-13 19:00:00        16.0              NaN\n",
      "2012-09-13 20:00:00        14.0              NaN\n",
      "2012-09-13 21:00:00        15.0              NaN\n",
      "2012-09-13 22:00:00        12.0              NaN\n",
      "2012-09-13 23:00:00        11.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-09-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-09-14 00:00:00        13.0              NaN\n",
      "2012-09-14 01:00:00        15.0              NaN\n",
      "2012-09-14 02:00:00        14.0              NaN\n",
      "2012-09-14 03:00:00        26.0              NaN\n",
      "2012-09-14 04:00:00        24.0              NaN\n",
      "...                         ...              ...\n",
      "2012-09-20 19:00:00       124.0              NaN\n",
      "2012-09-20 20:00:00       127.0              NaN\n",
      "2012-09-20 21:00:00       146.0              NaN\n",
      "2012-09-20 22:00:00       156.0              NaN\n",
      "2012-09-20 23:00:00       166.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-09-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-09-21 00:00:00       174.0              NaN\n",
      "2012-09-21 01:00:00       166.0              NaN\n",
      "2012-09-21 02:00:00       168.0              NaN\n",
      "2012-09-21 03:00:00       170.0              NaN\n",
      "2012-09-21 04:00:00       174.0              NaN\n",
      "...                         ...              ...\n",
      "2012-09-27 19:00:00        24.0              NaN\n",
      "2012-09-27 20:00:00         9.0              NaN\n",
      "2012-09-27 21:00:00         9.0              NaN\n",
      "2012-09-27 22:00:00         7.0              NaN\n",
      "2012-09-27 23:00:00         3.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-09-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-09-28 00:00:00         3.0              NaN\n",
      "2012-09-28 01:00:00         3.0              NaN\n",
      "2012-09-28 02:00:00         2.0              NaN\n",
      "2012-09-28 03:00:00         5.0              NaN\n",
      "2012-09-28 04:00:00         4.0              NaN\n",
      "...                         ...              ...\n",
      "2012-10-04 19:00:00        19.0              NaN\n",
      "2012-10-04 20:00:00        16.0              NaN\n",
      "2012-10-04 21:00:00        16.0              NaN\n",
      "2012-10-04 22:00:00        19.0              NaN\n",
      "2012-10-04 23:00:00        27.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-10-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-10-05 00:00:00        37.0              NaN\n",
      "2012-10-05 01:00:00        20.0              NaN\n",
      "2012-10-05 02:00:00        17.0              NaN\n",
      "2012-10-05 03:00:00        16.0              NaN\n",
      "2012-10-05 04:00:00        19.0              NaN\n",
      "...                         ...              ...\n",
      "2012-10-11 19:00:00       158.0              NaN\n",
      "2012-10-11 20:00:00       168.0              NaN\n",
      "2012-10-11 21:00:00       176.0              NaN\n",
      "2012-10-11 22:00:00       191.0              NaN\n",
      "2012-10-11 23:00:00       177.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-10-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-10-12 00:00:00       146.0              NaN\n",
      "2012-10-12 01:00:00       143.0              NaN\n",
      "2012-10-12 02:00:00       134.0              NaN\n",
      "2012-10-12 03:00:00       177.0              NaN\n",
      "2012-10-12 04:00:00       182.0              NaN\n",
      "...                         ...              ...\n",
      "2012-10-18 19:00:00        75.0              NaN\n",
      "2012-10-18 20:00:00       102.0              NaN\n",
      "2012-10-18 21:00:00       110.0              NaN\n",
      "2012-10-18 22:00:00       115.0              NaN\n",
      "2012-10-18 23:00:00       142.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-10-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-10-19 00:00:00       130.0              NaN\n",
      "2012-10-19 01:00:00       129.0              NaN\n",
      "2012-10-19 02:00:00        87.0              NaN\n",
      "2012-10-19 03:00:00        78.0              NaN\n",
      "2012-10-19 04:00:00        68.0              NaN\n",
      "...                         ...              ...\n",
      "2012-10-25 19:00:00       338.0              NaN\n",
      "2012-10-25 20:00:00       367.0              NaN\n",
      "2012-10-25 21:00:00       301.0              NaN\n",
      "2012-10-25 22:00:00       293.0              NaN\n",
      "2012-10-25 23:00:00       293.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-10-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-10-26 00:00:00       249.0              NaN\n",
      "2012-10-26 01:00:00       226.0              NaN\n",
      "2012-10-26 02:00:00       235.0              NaN\n",
      "2012-10-26 03:00:00       237.0              NaN\n",
      "2012-10-26 04:00:00       259.0              NaN\n",
      "...                         ...              ...\n",
      "2012-11-01 19:00:00       130.0              NaN\n",
      "2012-11-01 20:00:00       134.0              NaN\n",
      "2012-11-01 21:00:00       173.0              NaN\n",
      "2012-11-01 22:00:00       172.0              NaN\n",
      "2012-11-01 23:00:00       157.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-11-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-11-02 00:00:00       141.0              NaN\n",
      "2012-11-02 01:00:00       124.0              NaN\n",
      "2012-11-02 02:00:00       124.0              NaN\n",
      "2012-11-02 03:00:00       126.0              NaN\n",
      "2012-11-02 04:00:00       136.0              NaN\n",
      "...                         ...              ...\n",
      "2012-11-08 19:00:00        94.0              NaN\n",
      "2012-11-08 20:00:00       102.0              NaN\n",
      "2012-11-08 21:00:00       106.0              NaN\n",
      "2012-11-08 22:00:00       102.0              NaN\n",
      "2012-11-08 23:00:00       119.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-11-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-11-09 00:00:00       114.0              NaN\n",
      "2012-11-09 01:00:00       135.0              NaN\n",
      "2012-11-09 02:00:00       116.0              NaN\n",
      "2012-11-09 03:00:00       107.0              NaN\n",
      "2012-11-09 04:00:00        96.0              NaN\n",
      "...                         ...              ...\n",
      "2012-11-15 19:00:00       113.0              NaN\n",
      "2012-11-15 20:00:00       130.0              NaN\n",
      "2012-11-15 21:00:00       137.0              NaN\n",
      "2012-11-15 22:00:00       137.0              NaN\n",
      "2012-11-15 23:00:00       129.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-11-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-11-16 00:00:00       126.0              NaN\n",
      "2012-11-16 01:00:00       136.0              NaN\n",
      "2012-11-16 02:00:00       159.0              NaN\n",
      "2012-11-16 03:00:00       159.0              NaN\n",
      "2012-11-16 04:00:00       180.0              NaN\n",
      "...                         ...              ...\n",
      "2012-11-22 19:00:00        11.0              NaN\n",
      "2012-11-22 20:00:00         6.0              NaN\n",
      "2012-11-22 21:00:00         6.0              NaN\n",
      "2012-11-22 22:00:00        11.0              NaN\n",
      "2012-11-22 23:00:00        15.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-11-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-11-23 00:00:00        16.0              NaN\n",
      "2012-11-23 01:00:00        15.0              NaN\n",
      "2012-11-23 02:00:00        15.0              NaN\n",
      "2012-11-23 03:00:00        12.0              NaN\n",
      "2012-11-23 04:00:00        14.0              NaN\n",
      "...                         ...              ...\n",
      "2012-11-29 19:00:00        71.0              NaN\n",
      "2012-11-29 20:00:00        81.0              NaN\n",
      "2012-11-29 21:00:00        79.0              NaN\n",
      "2012-11-29 22:00:00       122.0              NaN\n",
      "2012-11-29 23:00:00       167.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-11-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-11-30 00:00:00       152.0              NaN\n",
      "2012-11-30 01:00:00        78.0              NaN\n",
      "2012-11-30 02:00:00        42.0              NaN\n",
      "2012-11-30 03:00:00        31.0              NaN\n",
      "2012-11-30 04:00:00        24.0              NaN\n",
      "...                         ...              ...\n",
      "2012-12-06 19:00:00       155.0              NaN\n",
      "2012-12-06 20:00:00       144.0              NaN\n",
      "2012-12-06 21:00:00       137.0              NaN\n",
      "2012-12-06 22:00:00       158.0              NaN\n",
      "2012-12-06 23:00:00       149.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-12-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-12-07 00:00:00       149.0              NaN\n",
      "2012-12-07 01:00:00       114.0              NaN\n",
      "2012-12-07 02:00:00        84.0              NaN\n",
      "2012-12-07 03:00:00        61.0              NaN\n",
      "2012-12-07 04:00:00        51.0              NaN\n",
      "...                         ...              ...\n",
      "2012-12-13 19:00:00       206.0              NaN\n",
      "2012-12-13 20:00:00       212.0              NaN\n",
      "2012-12-13 21:00:00       205.0              NaN\n",
      "2012-12-13 22:00:00       217.0              NaN\n",
      "2012-12-13 23:00:00       205.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-12-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-12-14 00:00:00       203.0              NaN\n",
      "2012-12-14 01:00:00       199.0              NaN\n",
      "2012-12-14 02:00:00       201.0              NaN\n",
      "2012-12-14 03:00:00       184.0              NaN\n",
      "2012-12-14 04:00:00       175.0              NaN\n",
      "...                         ...              ...\n",
      "2012-12-20 19:00:00       218.0              NaN\n",
      "2012-12-20 20:00:00       229.0              NaN\n",
      "2012-12-20 21:00:00       259.0              NaN\n",
      "2012-12-20 22:00:00       314.0              NaN\n",
      "2012-12-20 23:00:00       315.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-12-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-12-21 00:00:00       380.0              NaN\n",
      "2012-12-21 01:00:00       355.0              NaN\n",
      "2012-12-21 02:00:00       323.0              NaN\n",
      "2012-12-21 03:00:00       303.0              NaN\n",
      "2012-12-21 04:00:00       289.0              NaN\n",
      "...                         ...              ...\n",
      "2012-12-27 19:00:00         NaN              NaN\n",
      "2012-12-27 20:00:00         NaN              NaN\n",
      "2012-12-27 21:00:00         NaN              NaN\n",
      "2012-12-27 22:00:00         NaN              NaN\n",
      "2012-12-27 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2012-12-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2012-12-28 00:00:00         NaN              NaN\n",
      "2012-12-28 01:00:00         NaN              NaN\n",
      "2012-12-28 02:00:00         NaN              NaN\n",
      "2012-12-28 03:00:00         NaN              NaN\n",
      "2012-12-28 04:00:00         NaN              NaN\n",
      "...                         ...              ...\n",
      "2013-01-03 19:00:00        37.0              NaN\n",
      "2013-01-03 20:00:00        36.0              NaN\n",
      "2013-01-03 21:00:00        42.0              NaN\n",
      "2013-01-03 22:00:00        40.0              NaN\n",
      "2013-01-03 23:00:00        37.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-01-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-01-04 00:00:00        33.0              NaN\n",
      "2013-01-04 01:00:00        35.0              NaN\n",
      "2013-01-04 02:00:00        58.0              NaN\n",
      "2013-01-04 03:00:00        56.0              NaN\n",
      "2013-01-04 04:00:00        62.0              NaN\n",
      "...                         ...              ...\n",
      "2013-01-10 19:00:00       421.0              NaN\n",
      "2013-01-10 20:00:00       486.0              NaN\n",
      "2013-01-10 21:00:00       486.0              NaN\n",
      "2013-01-10 22:00:00       488.0              NaN\n",
      "2013-01-10 23:00:00       434.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-01-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-01-11 00:00:00       425.0              NaN\n",
      "2013-01-11 01:00:00       418.0              NaN\n",
      "2013-01-11 02:00:00       416.0              NaN\n",
      "2013-01-11 03:00:00       402.0              NaN\n",
      "2013-01-11 04:00:00       429.0              NaN\n",
      "...                         ...              ...\n",
      "2013-01-17 19:00:00       107.0             69.0\n",
      "2013-01-17 20:00:00       167.0            104.0\n",
      "2013-01-17 21:00:00       208.0            162.0\n",
      "2013-01-17 22:00:00       226.0            187.0\n",
      "2013-01-17 23:00:00       285.0            200.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-01-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-01-18 00:00:00       283.0            229.0\n",
      "2013-01-18 01:00:00       214.0            238.0\n",
      "2013-01-18 02:00:00       193.0            197.0\n",
      "2013-01-18 03:00:00       231.0            154.0\n",
      "2013-01-18 04:00:00       213.0            207.0\n",
      "...                         ...              ...\n",
      "2013-01-24 19:00:00        30.0             18.0\n",
      "2013-01-24 20:00:00        27.0             17.0\n",
      "2013-01-24 21:00:00        23.0             19.0\n",
      "2013-01-24 22:00:00        27.0             20.0\n",
      "2013-01-24 23:00:00        27.0             25.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-01-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-01-25 00:00:00        31.0             15.0\n",
      "2013-01-25 01:00:00        47.0             25.0\n",
      "2013-01-25 02:00:00        46.0             46.0\n",
      "2013-01-25 03:00:00        42.0             48.0\n",
      "2013-01-25 04:00:00        47.0             45.0\n",
      "...                         ...              ...\n",
      "2013-01-31 19:00:00       146.0            127.0\n",
      "2013-01-31 20:00:00       149.0            122.0\n",
      "2013-01-31 21:00:00       129.0            131.0\n",
      "2013-01-31 22:00:00       111.0            114.0\n",
      "2013-01-31 23:00:00       123.0             97.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-02-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-02-01 00:00:00       155.0             98.0\n",
      "2013-02-01 01:00:00       134.0              NaN\n",
      "2013-02-01 02:00:00        98.0            114.0\n",
      "2013-02-01 03:00:00        53.0              NaN\n",
      "2013-02-01 04:00:00        26.0             58.0\n",
      "...                         ...              ...\n",
      "2013-02-07 19:00:00        18.0             13.0\n",
      "2013-02-07 20:00:00        18.0             12.0\n",
      "2013-02-07 21:00:00        20.0             12.0\n",
      "2013-02-07 22:00:00        27.0             13.0\n",
      "2013-02-07 23:00:00        15.0             18.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-02-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-02-08 00:00:00        10.0              NaN\n",
      "2013-02-08 01:00:00        12.0              6.0\n",
      "2013-02-08 02:00:00         9.0             12.0\n",
      "2013-02-08 03:00:00        13.0              8.0\n",
      "2013-02-08 04:00:00        11.0              8.0\n",
      "...                         ...              ...\n",
      "2013-02-14 19:00:00        54.0             22.0\n",
      "2013-02-14 20:00:00       126.0             48.0\n",
      "2013-02-14 21:00:00       237.0            119.0\n",
      "2013-02-14 22:00:00       197.0            174.0\n",
      "2013-02-14 23:00:00        48.0            186.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-02-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-02-15 00:00:00        17.0             17.0\n",
      "2013-02-15 01:00:00        12.0              3.0\n",
      "2013-02-15 02:00:00        15.0              3.0\n",
      "2013-02-15 03:00:00        16.0              NaN\n",
      "2013-02-15 04:00:00        17.0              3.0\n",
      "...                         ...              ...\n",
      "2013-02-21 19:00:00       278.0            227.0\n",
      "2013-02-21 20:00:00       285.0            249.0\n",
      "2013-02-21 21:00:00       316.0              NaN\n",
      "2013-02-21 22:00:00       332.0            257.0\n",
      "2013-02-21 23:00:00       339.0            262.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-02-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-02-22 00:00:00       322.0            270.0\n",
      "2013-02-22 01:00:00       321.0            268.0\n",
      "2013-02-22 02:00:00       266.0            226.0\n",
      "2013-02-22 03:00:00       219.0            158.0\n",
      "2013-02-22 04:00:00       222.0            140.0\n",
      "...                         ...              ...\n",
      "2013-02-28 19:00:00        12.0              NaN\n",
      "2013-02-28 20:00:00         8.0              NaN\n",
      "2013-02-28 21:00:00         6.0              NaN\n",
      "2013-02-28 22:00:00         9.0              NaN\n",
      "2013-02-28 23:00:00         9.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-03-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-03-01 00:00:00         7.0              NaN\n",
      "2013-03-01 01:00:00         5.0              NaN\n",
      "2013-03-01 02:00:00         6.0              NaN\n",
      "2013-03-01 03:00:00         4.0              NaN\n",
      "2013-03-01 04:00:00         8.0              NaN\n",
      "...                         ...              ...\n",
      "2013-03-07 19:00:00       294.0            178.0\n",
      "2013-03-07 20:00:00       510.0            203.0\n",
      "2013-03-07 21:00:00       516.0            403.0\n",
      "2013-03-07 22:00:00       469.0            490.0\n",
      "2013-03-07 23:00:00       450.0            493.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-03-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-03-08 00:00:00       397.0            396.0\n",
      "2013-03-08 01:00:00       365.0            346.0\n",
      "2013-03-08 02:00:00       364.0            312.0\n",
      "2013-03-08 03:00:00       357.0            303.0\n",
      "2013-03-08 04:00:00       357.0            294.0\n",
      "...                         ...              ...\n",
      "2013-03-14 19:00:00       126.0             92.0\n",
      "2013-03-14 20:00:00       136.0             97.0\n",
      "2013-03-14 21:00:00       165.0            145.0\n",
      "2013-03-14 22:00:00       182.0            130.0\n",
      "2013-03-14 23:00:00       182.0            135.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-03-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-03-15 00:00:00       195.0            168.0\n",
      "2013-03-15 01:00:00       205.0            185.0\n",
      "2013-03-15 02:00:00       217.0            188.0\n",
      "2013-03-15 03:00:00       226.0            202.0\n",
      "2013-03-15 04:00:00       220.0            211.0\n",
      "...                         ...              ...\n",
      "2013-03-21 19:00:00       224.0            199.0\n",
      "2013-03-21 20:00:00       224.0            193.0\n",
      "2013-03-21 21:00:00       211.0            188.0\n",
      "2013-03-21 22:00:00       210.0            167.0\n",
      "2013-03-21 23:00:00       209.0            170.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-03-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-03-22 00:00:00       166.0            157.0\n",
      "2013-03-22 01:00:00       125.0            110.0\n",
      "2013-03-22 02:00:00        94.0             80.0\n",
      "2013-03-22 03:00:00        51.0            130.0\n",
      "2013-03-22 04:00:00        58.0             48.0\n",
      "...                         ...              ...\n",
      "2013-03-28 19:00:00        97.0             88.0\n",
      "2013-03-28 20:00:00       120.0            107.0\n",
      "2013-03-28 21:00:00       111.0            106.0\n",
      "2013-03-28 22:00:00       109.0             98.0\n",
      "2013-03-28 23:00:00       110.0             97.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-03-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-03-29 00:00:00        93.0            102.0\n",
      "2013-03-29 01:00:00       103.0             98.0\n",
      "2013-03-29 02:00:00       100.0            107.0\n",
      "2013-03-29 03:00:00       110.0             98.0\n",
      "2013-03-29 04:00:00       100.0             98.0\n",
      "...                         ...              ...\n",
      "2013-04-04 19:00:00        99.0             71.0\n",
      "2013-04-04 20:00:00        99.0             71.0\n",
      "2013-04-04 21:00:00       111.0             77.0\n",
      "2013-04-04 22:00:00       118.0             87.0\n",
      "2013-04-04 23:00:00       122.0             87.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-04-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-04-05 00:00:00       117.0            107.0\n",
      "2013-04-05 01:00:00       113.0             98.0\n",
      "2013-04-05 02:00:00       108.0             98.0\n",
      "2013-04-05 03:00:00       110.0             98.0\n",
      "2013-04-05 04:00:00       121.0            108.0\n",
      "...                         ...              ...\n",
      "2013-04-11 19:00:00        11.0             10.0\n",
      "2013-04-11 20:00:00        22.0             28.0\n",
      "2013-04-11 21:00:00        41.0             33.0\n",
      "2013-04-11 22:00:00        49.0             36.0\n",
      "2013-04-11 23:00:00        32.0             36.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-04-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-04-12 00:00:00        38.0             44.0\n",
      "2013-04-12 01:00:00        41.0             41.0\n",
      "2013-04-12 02:00:00        35.0             41.0\n",
      "2013-04-12 03:00:00        42.0             43.0\n",
      "2013-04-12 04:00:00        55.0             58.0\n",
      "...                         ...              ...\n",
      "2013-04-18 19:00:00        16.0             17.0\n",
      "2013-04-18 20:00:00        25.0             26.0\n",
      "2013-04-18 21:00:00        28.0             25.0\n",
      "2013-04-18 22:00:00        23.0             26.0\n",
      "2013-04-18 23:00:00        41.0             41.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-04-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-04-19 00:00:00        41.0             43.0\n",
      "2013-04-19 01:00:00        56.0             54.0\n",
      "2013-04-19 02:00:00        72.0             64.0\n",
      "2013-04-19 03:00:00        63.0             67.0\n",
      "2013-04-19 04:00:00        65.0             59.0\n",
      "...                         ...              ...\n",
      "2013-04-25 19:00:00        19.0              8.0\n",
      "2013-04-25 20:00:00        22.0              NaN\n",
      "2013-04-25 21:00:00        52.0             11.0\n",
      "2013-04-25 22:00:00        40.0             15.0\n",
      "2013-04-25 23:00:00        95.0             17.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-04-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-04-26 00:00:00        40.0             31.0\n",
      "2013-04-26 01:00:00        71.0             33.0\n",
      "2013-04-26 02:00:00        64.0             36.0\n",
      "2013-04-26 03:00:00        92.0             37.0\n",
      "2013-04-26 04:00:00        83.0             46.0\n",
      "...                         ...              ...\n",
      "2013-05-02 19:00:00        94.0             68.0\n",
      "2013-05-02 20:00:00        79.0             73.0\n",
      "2013-05-02 21:00:00        87.0             77.0\n",
      "2013-05-02 22:00:00        81.0             77.0\n",
      "2013-05-02 23:00:00        59.0             60.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-05-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-05-03 00:00:00        68.0             55.0\n",
      "2013-05-03 01:00:00        82.0             71.0\n",
      "2013-05-03 02:00:00        60.0             61.0\n",
      "2013-05-03 03:00:00        44.0             48.0\n",
      "2013-05-03 04:00:00        48.0             33.0\n",
      "...                         ...              ...\n",
      "2013-05-09 19:00:00        64.0             66.0\n",
      "2013-05-09 20:00:00        60.0             65.0\n",
      "2013-05-09 21:00:00        64.0             59.0\n",
      "2013-05-09 22:00:00        65.0             58.0\n",
      "2013-05-09 23:00:00        73.0             64.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-05-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-05-10 00:00:00        76.0             76.0\n",
      "2013-05-10 01:00:00        93.0             96.0\n",
      "2013-05-10 02:00:00        97.0             92.0\n",
      "2013-05-10 03:00:00       108.0             96.0\n",
      "2013-05-10 04:00:00       105.0             92.0\n",
      "...                         ...              ...\n",
      "2013-05-16 19:00:00        74.0             66.0\n",
      "2013-05-16 20:00:00        80.0             82.0\n",
      "2013-05-16 21:00:00        79.0             82.0\n",
      "2013-05-16 22:00:00        83.0             84.0\n",
      "2013-05-16 23:00:00        66.0             70.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-05-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-05-17 00:00:00        71.0             73.0\n",
      "2013-05-17 01:00:00        79.0             87.0\n",
      "2013-05-17 02:00:00        97.0             98.0\n",
      "2013-05-17 03:00:00        98.0            105.0\n",
      "2013-05-17 04:00:00       107.0            104.0\n",
      "...                         ...              ...\n",
      "2013-05-23 19:00:00        91.0             87.0\n",
      "2013-05-23 20:00:00       103.0             95.0\n",
      "2013-05-23 21:00:00       132.0             99.0\n",
      "2013-05-23 22:00:00       148.0            112.0\n",
      "2013-05-23 23:00:00       164.0            126.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-05-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-05-24 00:00:00       205.0            176.0\n",
      "2013-05-24 01:00:00       215.0            202.0\n",
      "2013-05-24 02:00:00       233.0            205.0\n",
      "2013-05-24 03:00:00       249.0            230.0\n",
      "2013-05-24 04:00:00       233.0            239.0\n",
      "...                         ...              ...\n",
      "2013-05-30 19:00:00        65.0             59.0\n",
      "2013-05-30 20:00:00        63.0             54.0\n",
      "2013-05-30 21:00:00        70.0             60.0\n",
      "2013-05-30 22:00:00        59.0             57.0\n",
      "2013-05-30 23:00:00        52.0             50.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-05-31 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-05-31 00:00:00        46.0             41.0\n",
      "2013-05-31 01:00:00        44.0             39.0\n",
      "2013-05-31 02:00:00        50.0             43.0\n",
      "2013-05-31 03:00:00        62.0             49.0\n",
      "2013-05-31 04:00:00        73.0             58.0\n",
      "...                         ...              ...\n",
      "2013-06-06 19:00:00       166.0            174.0\n",
      "2013-06-06 20:00:00       162.0            162.0\n",
      "2013-06-06 21:00:00       158.0            132.0\n",
      "2013-06-06 22:00:00       156.0            127.0\n",
      "2013-06-06 23:00:00       146.0            147.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-06-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-06-07 00:00:00       159.0            154.0\n",
      "2013-06-07 01:00:00       157.0            154.0\n",
      "2013-06-07 02:00:00       165.0            153.0\n",
      "2013-06-07 03:00:00       145.0            144.0\n",
      "2013-06-07 04:00:00       148.0            141.0\n",
      "...                         ...              ...\n",
      "2013-06-13 19:00:00       103.0              NaN\n",
      "2013-06-13 20:00:00       122.0              NaN\n",
      "2013-06-13 21:00:00        97.0              NaN\n",
      "2013-06-13 22:00:00        75.0              NaN\n",
      "2013-06-13 23:00:00        80.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-06-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-06-14 00:00:00        81.0              NaN\n",
      "2013-06-14 01:00:00        70.0              NaN\n",
      "2013-06-14 02:00:00        77.0              NaN\n",
      "2013-06-14 03:00:00        80.0              NaN\n",
      "2013-06-14 04:00:00        82.0              NaN\n",
      "...                         ...              ...\n",
      "2013-06-20 19:00:00       212.0            230.0\n",
      "2013-06-20 20:00:00       188.0            198.0\n",
      "2013-06-20 21:00:00       146.0            142.0\n",
      "2013-06-20 22:00:00       129.0            132.0\n",
      "2013-06-20 23:00:00       102.0            107.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-06-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-06-21 00:00:00       123.0            110.0\n",
      "2013-06-21 01:00:00       132.0            127.0\n",
      "2013-06-21 02:00:00       155.0            136.0\n",
      "2013-06-21 03:00:00       165.0            147.0\n",
      "2013-06-21 04:00:00       154.0            123.0\n",
      "...                         ...              ...\n",
      "2013-06-27 19:00:00        96.0             99.0\n",
      "2013-06-27 20:00:00       103.0            103.0\n",
      "2013-06-27 21:00:00       124.0            121.0\n",
      "2013-06-27 22:00:00       150.0            133.0\n",
      "2013-06-27 23:00:00       184.0            162.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-06-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-06-28 00:00:00       248.0            270.0\n",
      "2013-06-28 01:00:00       339.0            348.0\n",
      "2013-06-28 02:00:00       342.0            415.0\n",
      "2013-06-28 03:00:00       292.0            321.0\n",
      "2013-06-28 04:00:00       277.0            311.0\n",
      "...                         ...              ...\n",
      "2013-07-04 19:00:00        30.0             13.0\n",
      "2013-07-04 20:00:00        24.0             13.0\n",
      "2013-07-04 21:00:00        28.0             21.0\n",
      "2013-07-04 22:00:00        38.0             33.0\n",
      "2013-07-04 23:00:00        32.0             26.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-07-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-07-05 00:00:00        25.0             28.0\n",
      "2013-07-05 01:00:00        18.0             13.0\n",
      "2013-07-05 02:00:00        21.0             13.0\n",
      "2013-07-05 03:00:00        24.0             27.0\n",
      "2013-07-05 04:00:00        19.0             15.0\n",
      "...                         ...              ...\n",
      "2013-07-11 19:00:00        46.0              NaN\n",
      "2013-07-11 20:00:00        50.0              NaN\n",
      "2013-07-11 21:00:00        53.0              NaN\n",
      "2013-07-11 22:00:00        57.0              NaN\n",
      "2013-07-11 23:00:00        62.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-07-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-07-12 00:00:00        65.0              NaN\n",
      "2013-07-12 01:00:00        64.0              NaN\n",
      "2013-07-12 02:00:00        74.0              NaN\n",
      "2013-07-12 03:00:00        74.0              NaN\n",
      "2013-07-12 04:00:00        71.0              NaN\n",
      "...                         ...              ...\n",
      "2013-07-18 19:00:00        91.0             93.0\n",
      "2013-07-18 20:00:00        99.0            106.0\n",
      "2013-07-18 21:00:00        77.0            100.0\n",
      "2013-07-18 22:00:00        86.0             84.0\n",
      "2013-07-18 23:00:00        93.0             94.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-07-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-07-19 00:00:00       107.0            111.0\n",
      "2013-07-19 01:00:00       126.0            137.0\n",
      "2013-07-19 02:00:00       141.0            153.0\n",
      "2013-07-19 03:00:00       146.0            163.0\n",
      "2013-07-19 04:00:00       160.0            176.0\n",
      "...                         ...              ...\n",
      "2013-07-25 19:00:00        66.0             83.0\n",
      "2013-07-25 20:00:00        69.0             79.0\n",
      "2013-07-25 21:00:00        53.0             62.0\n",
      "2013-07-25 22:00:00        49.0             46.0\n",
      "2013-07-25 23:00:00        43.0             40.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-07-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-07-26 00:00:00        42.0             34.0\n",
      "2013-07-26 01:00:00        40.0             28.0\n",
      "2013-07-26 02:00:00        32.0             22.0\n",
      "2013-07-26 03:00:00        41.0             35.0\n",
      "2013-07-26 04:00:00        40.0             36.0\n",
      "...                         ...              ...\n",
      "2013-08-01 19:00:00        59.0             63.0\n",
      "2013-08-01 20:00:00        55.0             56.0\n",
      "2013-08-01 21:00:00        68.0             62.0\n",
      "2013-08-01 22:00:00        73.0             59.0\n",
      "2013-08-01 23:00:00        76.0             58.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-08-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-08-02 00:00:00        80.0             64.0\n",
      "2013-08-02 01:00:00        84.0             76.0\n",
      "2013-08-02 02:00:00        90.0             85.0\n",
      "2013-08-02 03:00:00        79.0             82.0\n",
      "2013-08-02 04:00:00        69.0             82.0\n",
      "...                         ...              ...\n",
      "2013-08-08 19:00:00        59.0             60.0\n",
      "2013-08-08 20:00:00        58.0             67.0\n",
      "2013-08-08 21:00:00        27.0             33.0\n",
      "2013-08-08 22:00:00        50.0             40.0\n",
      "2013-08-08 23:00:00        30.0             33.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-08-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-08-09 00:00:00        31.0             31.0\n",
      "2013-08-09 01:00:00        26.0             23.0\n",
      "2013-08-09 02:00:00        24.0             26.0\n",
      "2013-08-09 03:00:00        31.0             22.0\n",
      "2013-08-09 04:00:00        23.0             22.0\n",
      "...                         ...              ...\n",
      "2013-08-15 19:00:00       151.0            203.0\n",
      "2013-08-15 20:00:00       164.0            198.0\n",
      "2013-08-15 21:00:00       160.0            198.0\n",
      "2013-08-15 22:00:00       160.0            200.0\n",
      "2013-08-15 23:00:00       179.0            209.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-08-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-08-16 00:00:00        55.0             49.0\n",
      "2013-08-16 01:00:00        60.0             32.0\n",
      "2013-08-16 02:00:00        65.0             43.0\n",
      "2013-08-16 03:00:00        61.0             67.0\n",
      "2013-08-16 04:00:00        60.0             74.0\n",
      "...                         ...              ...\n",
      "2013-08-22 19:00:00       117.0            105.0\n",
      "2013-08-22 20:00:00       123.0            109.0\n",
      "2013-08-22 21:00:00       141.0            133.0\n",
      "2013-08-22 22:00:00       121.0            147.0\n",
      "2013-08-22 23:00:00       110.0            147.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-08-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-08-23 00:00:00       116.0            117.0\n",
      "2013-08-23 01:00:00       148.0            143.0\n",
      "2013-08-23 02:00:00       150.0            133.0\n",
      "2013-08-23 03:00:00       114.0            139.0\n",
      "2013-08-23 04:00:00        89.0             91.0\n",
      "...                         ...              ...\n",
      "2013-08-29 19:00:00        15.0              9.0\n",
      "2013-08-29 20:00:00        12.0             10.0\n",
      "2013-08-29 21:00:00        16.0             10.0\n",
      "2013-08-29 22:00:00        14.0              7.0\n",
      "2013-08-29 23:00:00        14.0              3.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-08-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-08-30 00:00:00         8.0              3.0\n",
      "2013-08-30 01:00:00        10.0              3.0\n",
      "2013-08-30 02:00:00        10.0              5.0\n",
      "2013-08-30 03:00:00         9.0              5.0\n",
      "2013-08-30 04:00:00         6.0              3.0\n",
      "...                         ...              ...\n",
      "2013-09-05 19:00:00        52.0             47.0\n",
      "2013-09-05 20:00:00        57.0             46.0\n",
      "2013-09-05 21:00:00        48.0             40.0\n",
      "2013-09-05 22:00:00        60.0             47.0\n",
      "2013-09-05 23:00:00        62.0             40.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-09-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-09-06 00:00:00        61.0             56.0\n",
      "2013-09-06 01:00:00        68.0             56.0\n",
      "2013-09-06 02:00:00        67.0             58.0\n",
      "2013-09-06 03:00:00        72.0             48.0\n",
      "2013-09-06 04:00:00        65.0             59.0\n",
      "...                         ...              ...\n",
      "2013-09-12 19:00:00       198.0            200.0\n",
      "2013-09-12 20:00:00       204.0            207.0\n",
      "2013-09-12 21:00:00       213.0              NaN\n",
      "2013-09-12 22:00:00       132.0            151.0\n",
      "2013-09-12 23:00:00        46.0             35.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-09-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-09-13 00:00:00        86.0             43.0\n",
      "2013-09-13 01:00:00        69.0             65.0\n",
      "2013-09-13 02:00:00        60.0             53.0\n",
      "2013-09-13 03:00:00        59.0             47.0\n",
      "2013-09-13 04:00:00        54.0             27.0\n",
      "...                         ...              ...\n",
      "2013-09-19 19:00:00       128.0            130.0\n",
      "2013-09-19 20:00:00       129.0            131.0\n",
      "2013-09-19 21:00:00       140.0            130.0\n",
      "2013-09-19 22:00:00       138.0            129.0\n",
      "2013-09-19 23:00:00       153.0            116.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-09-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-09-20 00:00:00       156.0             92.0\n",
      "2013-09-20 01:00:00       153.0            137.0\n",
      "2013-09-20 02:00:00       147.0            115.0\n",
      "2013-09-20 03:00:00       131.0            127.0\n",
      "2013-09-20 04:00:00       114.0            117.0\n",
      "...                         ...              ...\n",
      "2013-09-26 19:00:00        69.0             58.0\n",
      "2013-09-26 20:00:00       107.0             64.0\n",
      "2013-09-26 21:00:00       114.0             71.0\n",
      "2013-09-26 22:00:00       138.0             71.0\n",
      "2013-09-26 23:00:00       187.0            119.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-09-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-09-27 00:00:00       184.0            140.0\n",
      "2013-09-27 01:00:00       146.0            145.0\n",
      "2013-09-27 02:00:00       139.0            118.0\n",
      "2013-09-27 03:00:00       143.0            122.0\n",
      "2013-09-27 04:00:00       129.0            117.0\n",
      "...                         ...              ...\n",
      "2013-10-03 19:00:00        58.0             41.0\n",
      "2013-10-03 20:00:00        57.0             42.0\n",
      "2013-10-03 21:00:00        55.0             50.0\n",
      "2013-10-03 22:00:00        42.0             31.0\n",
      "2013-10-03 23:00:00        40.0             40.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-10-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-10-04 00:00:00        47.0             43.0\n",
      "2013-10-04 01:00:00        58.0             48.0\n",
      "2013-10-04 02:00:00        55.0             54.0\n",
      "2013-10-04 03:00:00        58.0             59.0\n",
      "2013-10-04 04:00:00        67.0             66.0\n",
      "...                         ...              ...\n",
      "2013-10-10 19:00:00        10.0              7.0\n",
      "2013-10-10 20:00:00        10.0              7.0\n",
      "2013-10-10 21:00:00         8.0              3.0\n",
      "2013-10-10 22:00:00        16.0              6.0\n",
      "2013-10-10 23:00:00        22.0              6.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-10-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-10-11 00:00:00        23.0              4.0\n",
      "2013-10-11 01:00:00        27.0              6.0\n",
      "2013-10-11 02:00:00        19.0             10.0\n",
      "2013-10-11 03:00:00        19.0             21.0\n",
      "2013-10-11 04:00:00        21.0             15.0\n",
      "...                         ...              ...\n",
      "2013-10-17 19:00:00       175.0            165.0\n",
      "2013-10-17 20:00:00       189.0            146.0\n",
      "2013-10-17 21:00:00       206.0            164.0\n",
      "2013-10-17 22:00:00       216.0            192.0\n",
      "2013-10-17 23:00:00       224.0            195.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-10-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-10-18 00:00:00       231.0            172.0\n",
      "2013-10-18 01:00:00       233.0            180.0\n",
      "2013-10-18 02:00:00       262.0            216.0\n",
      "2013-10-18 03:00:00       282.0            222.0\n",
      "2013-10-18 04:00:00       259.0            220.0\n",
      "...                         ...              ...\n",
      "2013-10-24 19:00:00        38.0             34.0\n",
      "2013-10-24 20:00:00        38.0             32.0\n",
      "2013-10-24 21:00:00        41.0             32.0\n",
      "2013-10-24 22:00:00        45.0             34.0\n",
      "2013-10-24 23:00:00        37.0             34.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-10-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-10-25 00:00:00        42.0             36.0\n",
      "2013-10-25 01:00:00        40.0             29.0\n",
      "2013-10-25 02:00:00        41.0             34.0\n",
      "2013-10-25 03:00:00        40.0             29.0\n",
      "2013-10-25 04:00:00        41.0             29.0\n",
      "...                         ...              ...\n",
      "2013-10-31 19:00:00       218.0            221.0\n",
      "2013-10-31 20:00:00       211.0            221.0\n",
      "2013-10-31 21:00:00       229.0            179.0\n",
      "2013-10-31 22:00:00       280.0            179.0\n",
      "2013-10-31 23:00:00       274.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-11-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-11-01 00:00:00       289.0            246.0\n",
      "2013-11-01 01:00:00       311.0            306.0\n",
      "2013-11-01 02:00:00       299.0            300.0\n",
      "2013-11-01 03:00:00       308.0            321.0\n",
      "2013-11-01 04:00:00       297.0            287.0\n",
      "...                         ...              ...\n",
      "2013-11-07 19:00:00        89.0             85.0\n",
      "2013-11-07 20:00:00       108.0             79.0\n",
      "2013-11-07 21:00:00       112.0             94.0\n",
      "2013-11-07 22:00:00       118.0            100.0\n",
      "2013-11-07 23:00:00       164.0            104.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-11-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-11-08 00:00:00       196.0            149.0\n",
      "2013-11-08 01:00:00       188.0            174.0\n",
      "2013-11-08 02:00:00       217.0            177.0\n",
      "2013-11-08 03:00:00       174.0            180.0\n",
      "2013-11-08 04:00:00       167.0            171.0\n",
      "...                         ...              ...\n",
      "2013-11-14 19:00:00        65.0             70.0\n",
      "2013-11-14 20:00:00        76.0             67.0\n",
      "2013-11-14 21:00:00        72.0             75.0\n",
      "2013-11-14 22:00:00        78.0             73.0\n",
      "2013-11-14 23:00:00        81.0             70.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-11-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-11-15 00:00:00        88.0             74.0\n",
      "2013-11-15 01:00:00        93.0             76.0\n",
      "2013-11-15 02:00:00       106.0             78.0\n",
      "2013-11-15 03:00:00       108.0             97.0\n",
      "2013-11-15 04:00:00       115.0            116.0\n",
      "...                         ...              ...\n",
      "2013-11-21 19:00:00       166.0            163.0\n",
      "2013-11-21 20:00:00       206.0            194.0\n",
      "2013-11-21 21:00:00       218.0            206.0\n",
      "2013-11-21 22:00:00       226.0            197.0\n",
      "2013-11-21 23:00:00       232.0            199.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-11-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-11-22 00:00:00       239.0            195.0\n",
      "2013-11-22 01:00:00       230.0            203.0\n",
      "2013-11-22 02:00:00       165.0            184.0\n",
      "2013-11-22 03:00:00       163.0            128.0\n",
      "2013-11-22 04:00:00       128.0            103.0\n",
      "...                         ...              ...\n",
      "2013-11-28 19:00:00        91.0             63.0\n",
      "2013-11-28 20:00:00        85.0             72.0\n",
      "2013-11-28 21:00:00        88.0             58.0\n",
      "2013-11-28 22:00:00        94.0             41.0\n",
      "2013-11-28 23:00:00        46.0             32.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-11-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-11-29 00:00:00        33.0             30.0\n",
      "2013-11-29 01:00:00        38.0             22.0\n",
      "2013-11-29 02:00:00        25.0             23.0\n",
      "2013-11-29 03:00:00        21.0             12.0\n",
      "2013-11-29 04:00:00        27.0             15.0\n",
      "...                         ...              ...\n",
      "2013-12-05 19:00:00        42.0             47.0\n",
      "2013-12-05 20:00:00        59.0             49.0\n",
      "2013-12-05 21:00:00        66.0             62.0\n",
      "2013-12-05 22:00:00        77.0             68.0\n",
      "2013-12-05 23:00:00        84.0             80.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-12-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-12-06 00:00:00        91.0             84.0\n",
      "2013-12-06 01:00:00       112.0            105.0\n",
      "2013-12-06 02:00:00       130.0            126.0\n",
      "2013-12-06 03:00:00       139.0            131.0\n",
      "2013-12-06 04:00:00       155.0            143.0\n",
      "...                         ...              ...\n",
      "2013-12-12 19:00:00        23.0             12.0\n",
      "2013-12-12 20:00:00        12.0              9.0\n",
      "2013-12-12 21:00:00        16.0             13.0\n",
      "2013-12-12 22:00:00        38.0             31.0\n",
      "2013-12-12 23:00:00        49.0             35.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-12-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-12-13 00:00:00        45.0             41.0\n",
      "2013-12-13 01:00:00        48.0             43.0\n",
      "2013-12-13 02:00:00        45.0             44.0\n",
      "2013-12-13 03:00:00        39.0             35.0\n",
      "2013-12-13 04:00:00        43.0             32.0\n",
      "...                         ...              ...\n",
      "2013-12-19 19:00:00        80.0             44.0\n",
      "2013-12-19 20:00:00        93.0             79.0\n",
      "2013-12-19 21:00:00        95.0             79.0\n",
      "2013-12-19 22:00:00       107.0             81.0\n",
      "2013-12-19 23:00:00       152.0             81.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-12-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-12-20 00:00:00       117.0            106.0\n",
      "2013-12-20 01:00:00        40.0             82.0\n",
      "2013-12-20 02:00:00        23.0             48.0\n",
      "2013-12-20 03:00:00        17.0             20.0\n",
      "2013-12-20 04:00:00        25.0             16.0\n",
      "...                         ...              ...\n",
      "2013-12-26 19:00:00        16.0             14.0\n",
      "2013-12-26 20:00:00        13.0             10.0\n",
      "2013-12-26 21:00:00        17.0             15.0\n",
      "2013-12-26 22:00:00        15.0             10.0\n",
      "2013-12-26 23:00:00        13.0             14.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2013-12-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2013-12-27 00:00:00        23.0             11.0\n",
      "2013-12-27 01:00:00        16.0             12.0\n",
      "2013-12-27 02:00:00        18.0             11.0\n",
      "2013-12-27 03:00:00        12.0             12.0\n",
      "2013-12-27 04:00:00        16.0             12.0\n",
      "...                         ...              ...\n",
      "2014-01-02 19:00:00       239.0            194.0\n",
      "2014-01-02 20:00:00       232.0            204.0\n",
      "2014-01-02 21:00:00       242.0            231.0\n",
      "2014-01-02 22:00:00       269.0            227.0\n",
      "2014-01-02 23:00:00       264.0            238.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-01-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-01-03 00:00:00       220.0            189.0\n",
      "2014-01-03 01:00:00       146.0            126.0\n",
      "2014-01-03 02:00:00        34.0             44.0\n",
      "2014-01-03 03:00:00        34.0             27.0\n",
      "2014-01-03 04:00:00        35.0             32.0\n",
      "...                         ...              ...\n",
      "2014-01-09 19:00:00        56.0             48.0\n",
      "2014-01-09 20:00:00        58.0             52.0\n",
      "2014-01-09 21:00:00        66.0             53.0\n",
      "2014-01-09 22:00:00        73.0             63.0\n",
      "2014-01-09 23:00:00        83.0             75.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-01-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-01-10 00:00:00        81.0             73.0\n",
      "2014-01-10 01:00:00        91.0             63.0\n",
      "2014-01-10 02:00:00       104.0             87.0\n",
      "2014-01-10 03:00:00        88.0             89.0\n",
      "2014-01-10 04:00:00        85.0              NaN\n",
      "...                         ...              ...\n",
      "2014-01-16 19:00:00       437.0            400.0\n",
      "2014-01-16 20:00:00       422.0            376.0\n",
      "2014-01-16 21:00:00       320.0            278.0\n",
      "2014-01-16 22:00:00       346.0            290.0\n",
      "2014-01-16 23:00:00       351.0            305.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-01-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-01-17 00:00:00       297.0            257.0\n",
      "2014-01-17 01:00:00       290.0            250.0\n",
      "2014-01-17 02:00:00       250.0            231.0\n",
      "2014-01-17 03:00:00       170.0            167.0\n",
      "2014-01-17 04:00:00       103.0            100.0\n",
      "...                         ...              ...\n",
      "2014-01-23 19:00:00       301.0            254.0\n",
      "2014-01-23 20:00:00       325.0            282.0\n",
      "2014-01-23 21:00:00       319.0            289.0\n",
      "2014-01-23 22:00:00       335.0            272.0\n",
      "2014-01-23 23:00:00       301.0            263.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-01-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-01-24 00:00:00       312.0            262.0\n",
      "2014-01-24 01:00:00       316.0            270.0\n",
      "2014-01-24 02:00:00       296.0            264.0\n",
      "2014-01-24 03:00:00       270.0            230.0\n",
      "2014-01-24 04:00:00       252.0            223.0\n",
      "...                         ...              ...\n",
      "2014-01-30 19:00:00       123.0            115.0\n",
      "2014-01-30 20:00:00       142.0            126.0\n",
      "2014-01-30 21:00:00       157.0            153.0\n",
      "2014-01-30 22:00:00       190.0            154.0\n",
      "2014-01-30 23:00:00       137.0            144.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-01-31 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-01-31 00:00:00       469.0            426.0\n",
      "2014-01-31 01:00:00       344.0            361.0\n",
      "2014-01-31 02:00:00       281.0            241.0\n",
      "2014-01-31 03:00:00       209.0            194.0\n",
      "2014-01-31 04:00:00       150.0            157.0\n",
      "...                         ...              ...\n",
      "2014-02-06 19:00:00       130.0            114.0\n",
      "2014-02-06 20:00:00       155.0            132.0\n",
      "2014-02-06 21:00:00       131.0            111.0\n",
      "2014-02-06 22:00:00        93.0             77.0\n",
      "2014-02-06 23:00:00        91.0             80.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-02-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-02-07 00:00:00        86.0             78.0\n",
      "2014-02-07 01:00:00       101.0             87.0\n",
      "2014-02-07 02:00:00       105.0             96.0\n",
      "2014-02-07 03:00:00       110.0             99.0\n",
      "2014-02-07 04:00:00       112.0             99.0\n",
      "...                         ...              ...\n",
      "2014-02-13 19:00:00       239.0            207.0\n",
      "2014-02-13 20:00:00       250.0            208.0\n",
      "2014-02-13 21:00:00       243.0            216.0\n",
      "2014-02-13 22:00:00       251.0            216.0\n",
      "2014-02-13 23:00:00       263.0            221.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-02-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-02-14 00:00:00       262.0            219.0\n",
      "2014-02-14 01:00:00       261.0            220.0\n",
      "2014-02-14 02:00:00       276.0            226.0\n",
      "2014-02-14 03:00:00       264.0            234.0\n",
      "2014-02-14 04:00:00       259.0            232.0\n",
      "...                         ...              ...\n",
      "2014-02-20 19:00:00       400.0            333.0\n",
      "2014-02-20 20:00:00       394.0            349.0\n",
      "2014-02-20 21:00:00       448.0            367.0\n",
      "2014-02-20 22:00:00       424.0            342.0\n",
      "2014-02-20 23:00:00       400.0            320.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-02-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-02-21 00:00:00       398.0            295.0\n",
      "2014-02-21 01:00:00       383.0            264.0\n",
      "2014-02-21 02:00:00       357.0            267.0\n",
      "2014-02-21 03:00:00       367.0            286.0\n",
      "2014-02-21 04:00:00       335.0            272.0\n",
      "...                         ...              ...\n",
      "2014-02-27 19:00:00        31.0              NaN\n",
      "2014-02-27 20:00:00        45.0              NaN\n",
      "2014-02-27 21:00:00        42.0             32.0\n",
      "2014-02-27 22:00:00        53.0             43.0\n",
      "2014-02-27 23:00:00        42.0             38.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-02-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-02-28 00:00:00        34.0             34.0\n",
      "2014-02-28 01:00:00       130.0             98.0\n",
      "2014-02-28 02:00:00       114.0            142.0\n",
      "2014-02-28 03:00:00        61.0             64.0\n",
      "2014-02-28 04:00:00        48.0             45.0\n",
      "...                         ...              ...\n",
      "2014-03-06 19:00:00        29.0             29.0\n",
      "2014-03-06 20:00:00        41.0             36.0\n",
      "2014-03-06 21:00:00        41.0             39.0\n",
      "2014-03-06 22:00:00        53.0             43.0\n",
      "2014-03-06 23:00:00        67.0             58.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-03-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-03-07 00:00:00        70.0             56.0\n",
      "2014-03-07 01:00:00        84.0             72.0\n",
      "2014-03-07 02:00:00        91.0             77.0\n",
      "2014-03-07 03:00:00        99.0             85.0\n",
      "2014-03-07 04:00:00        97.0             89.0\n",
      "...                         ...              ...\n",
      "2014-03-13 19:00:00        40.0             49.0\n",
      "2014-03-13 20:00:00        39.0             43.0\n",
      "2014-03-13 21:00:00        22.0             27.0\n",
      "2014-03-13 22:00:00        37.0             32.0\n",
      "2014-03-13 23:00:00        24.0             28.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-03-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-03-14 00:00:00        19.0             17.0\n",
      "2014-03-14 01:00:00        11.0             17.0\n",
      "2014-03-14 02:00:00        13.0             10.0\n",
      "2014-03-14 03:00:00        11.0              9.0\n",
      "2014-03-14 04:00:00        10.0              7.0\n",
      "...                         ...              ...\n",
      "2014-03-20 19:00:00        18.0             11.0\n",
      "2014-03-20 20:00:00        11.0             14.0\n",
      "2014-03-20 21:00:00        11.0             13.0\n",
      "2014-03-20 22:00:00         7.0              6.0\n",
      "2014-03-20 23:00:00         6.0              9.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-03-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-03-21 00:00:00         5.0              4.0\n",
      "2014-03-21 01:00:00         7.0              4.0\n",
      "2014-03-21 02:00:00        12.0              8.0\n",
      "2014-03-21 03:00:00        13.0             13.0\n",
      "2014-03-21 04:00:00        17.0             11.0\n",
      "...                         ...              ...\n",
      "2014-03-27 19:00:00       107.0             97.0\n",
      "2014-03-27 20:00:00       111.0            103.0\n",
      "2014-03-27 21:00:00       125.0            110.0\n",
      "2014-03-27 22:00:00       123.0            107.0\n",
      "2014-03-27 23:00:00       145.0            107.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-03-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-03-28 00:00:00       153.0            126.0\n",
      "2014-03-28 01:00:00       159.0            126.0\n",
      "2014-03-28 02:00:00       154.0              NaN\n",
      "2014-03-28 03:00:00       191.0            158.0\n",
      "2014-03-28 04:00:00       195.0            162.0\n",
      "...                         ...              ...\n",
      "2014-04-03 19:00:00        18.0             11.0\n",
      "2014-04-03 20:00:00        22.0             22.0\n",
      "2014-04-03 21:00:00        20.0             20.0\n",
      "2014-04-03 22:00:00        33.0             36.0\n",
      "2014-04-03 23:00:00        40.0             39.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-04-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-04-04 00:00:00        35.0             37.0\n",
      "2014-04-04 01:00:00        28.0             28.0\n",
      "2014-04-04 02:00:00        29.0             31.0\n",
      "2014-04-04 03:00:00        32.0             31.0\n",
      "2014-04-04 04:00:00        36.0             38.0\n",
      "...                         ...              ...\n",
      "2014-04-10 19:00:00        63.0             57.0\n",
      "2014-04-10 20:00:00        65.0             60.0\n",
      "2014-04-10 21:00:00        72.0             67.0\n",
      "2014-04-10 22:00:00        67.0             67.0\n",
      "2014-04-10 23:00:00        51.0             67.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-04-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-04-11 00:00:00        67.0              NaN\n",
      "2014-04-11 01:00:00        64.0             60.0\n",
      "2014-04-11 02:00:00        71.0             62.0\n",
      "2014-04-11 03:00:00        76.0             66.0\n",
      "2014-04-11 04:00:00        69.0             68.0\n",
      "...                         ...              ...\n",
      "2014-04-17 19:00:00       127.0            124.0\n",
      "2014-04-17 20:00:00       141.0            122.0\n",
      "2014-04-17 21:00:00       137.0            101.0\n",
      "2014-04-17 22:00:00       151.0            113.0\n",
      "2014-04-17 23:00:00       152.0            145.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-04-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-04-18 00:00:00       148.0            126.0\n",
      "2014-04-18 01:00:00       151.0            120.0\n",
      "2014-04-18 02:00:00       156.0            116.0\n",
      "2014-04-18 03:00:00       143.0            119.0\n",
      "2014-04-18 04:00:00       141.0            123.0\n",
      "...                         ...              ...\n",
      "2014-04-24 19:00:00       117.0             88.0\n",
      "2014-04-24 20:00:00       118.0            113.0\n",
      "2014-04-24 21:00:00       121.0            101.0\n",
      "2014-04-24 22:00:00       140.0            100.0\n",
      "2014-04-24 23:00:00       139.0            132.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-04-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-04-25 00:00:00       136.0            135.0\n",
      "2014-04-25 01:00:00       143.0            116.0\n",
      "2014-04-25 02:00:00       155.0            138.0\n",
      "2014-04-25 03:00:00       141.0            122.0\n",
      "2014-04-25 04:00:00       110.0            126.0\n",
      "...                         ...              ...\n",
      "2014-05-01 19:00:00       113.0             69.0\n",
      "2014-05-01 20:00:00        54.0             49.0\n",
      "2014-05-01 21:00:00        51.0             31.0\n",
      "2014-05-01 22:00:00        48.0             37.0\n",
      "2014-05-01 23:00:00        47.0             35.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-05-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-05-02 00:00:00        44.0             35.0\n",
      "2014-05-02 01:00:00        34.0             31.0\n",
      "2014-05-02 02:00:00        28.0             25.0\n",
      "2014-05-02 03:00:00        34.0             53.0\n",
      "2014-05-02 04:00:00        34.0             29.0\n",
      "...                         ...              ...\n",
      "2014-05-08 19:00:00        68.0             63.0\n",
      "2014-05-08 20:00:00        63.0             62.0\n",
      "2014-05-08 21:00:00        55.0             57.0\n",
      "2014-05-08 22:00:00        70.0             64.0\n",
      "2014-05-08 23:00:00        84.0             65.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-05-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-05-09 00:00:00        65.0             71.0\n",
      "2014-05-09 01:00:00        65.0             57.0\n",
      "2014-05-09 02:00:00        70.0             62.0\n",
      "2014-05-09 03:00:00        70.0             68.0\n",
      "2014-05-09 04:00:00        75.0             80.0\n",
      "...                         ...              ...\n",
      "2014-05-15 19:00:00        95.0             75.0\n",
      "2014-05-15 20:00:00       105.0             98.0\n",
      "2014-05-15 21:00:00        94.0             86.0\n",
      "2014-05-15 22:00:00       121.0             94.0\n",
      "2014-05-15 23:00:00       203.0             85.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-05-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-05-16 00:00:00        84.0             77.0\n",
      "2014-05-16 01:00:00        96.0             68.0\n",
      "2014-05-16 02:00:00        83.0             69.0\n",
      "2014-05-16 03:00:00        85.0             70.0\n",
      "2014-05-16 04:00:00        93.0             81.0\n",
      "...                         ...              ...\n",
      "2014-05-22 19:00:00        56.0             58.0\n",
      "2014-05-22 20:00:00        64.0             60.0\n",
      "2014-05-22 21:00:00        65.0             59.0\n",
      "2014-05-22 22:00:00        63.0             60.0\n",
      "2014-05-22 23:00:00        73.0             62.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-05-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-05-23 00:00:00        74.0             67.0\n",
      "2014-05-23 01:00:00        84.0             69.0\n",
      "2014-05-23 02:00:00        80.0             71.0\n",
      "2014-05-23 03:00:00        96.0             89.0\n",
      "2014-05-23 04:00:00       109.0            103.0\n",
      "...                         ...              ...\n",
      "2014-05-29 19:00:00        38.0             24.0\n",
      "2014-05-29 20:00:00        63.0             41.0\n",
      "2014-05-29 21:00:00        63.0             45.0\n",
      "2014-05-29 22:00:00        63.0             53.0\n",
      "2014-05-29 23:00:00        79.0             68.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-05-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-05-30 00:00:00        71.0             79.0\n",
      "2014-05-30 01:00:00        96.0             89.0\n",
      "2014-05-30 02:00:00       102.0            100.0\n",
      "2014-05-30 03:00:00       113.0            109.0\n",
      "2014-05-30 04:00:00        73.0             98.0\n",
      "...                         ...              ...\n",
      "2014-06-05 19:00:00        60.0             60.0\n",
      "2014-06-05 20:00:00        68.0             56.0\n",
      "2014-06-05 21:00:00        70.0             61.0\n",
      "2014-06-05 22:00:00        81.0             78.0\n",
      "2014-06-05 23:00:00         NaN             85.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-06-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-06-06 00:00:00        90.0             78.0\n",
      "2014-06-06 01:00:00       108.0             97.0\n",
      "2014-06-06 02:00:00       109.0            103.0\n",
      "2014-06-06 03:00:00       106.0            109.0\n",
      "2014-06-06 04:00:00        96.0             82.0\n",
      "...                         ...              ...\n",
      "2014-06-12 19:00:00        40.0             30.0\n",
      "2014-06-12 20:00:00        44.0             32.0\n",
      "2014-06-12 21:00:00        38.0             42.0\n",
      "2014-06-12 22:00:00        50.0             39.0\n",
      "2014-06-12 23:00:00        64.0             47.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-06-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-06-13 00:00:00        99.0             77.0\n",
      "2014-06-13 01:00:00       110.0             87.0\n",
      "2014-06-13 02:00:00       105.0            107.0\n",
      "2014-06-13 03:00:00       108.0            100.0\n",
      "2014-06-13 04:00:00       121.0            102.0\n",
      "...                         ...              ...\n",
      "2014-06-19 19:00:00        79.0             67.0\n",
      "2014-06-19 20:00:00        73.0             64.0\n",
      "2014-06-19 21:00:00        72.0             70.0\n",
      "2014-06-19 22:00:00        74.0             73.0\n",
      "2014-06-19 23:00:00        78.0             69.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-06-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-06-20 00:00:00        89.0             83.0\n",
      "2014-06-20 01:00:00        86.0             73.0\n",
      "2014-06-20 02:00:00        59.0             47.0\n",
      "2014-06-20 03:00:00        39.0             31.0\n",
      "2014-06-20 04:00:00        48.0             35.0\n",
      "...                         ...              ...\n",
      "2014-06-26 19:00:00        62.0             61.0\n",
      "2014-06-26 20:00:00        61.0             50.0\n",
      "2014-06-26 21:00:00        68.0             50.0\n",
      "2014-06-26 22:00:00        75.0             58.0\n",
      "2014-06-26 23:00:00        62.0             75.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-06-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-06-27 00:00:00        31.0             63.0\n",
      "2014-06-27 01:00:00        39.0             40.0\n",
      "2014-06-27 02:00:00        46.0             44.0\n",
      "2014-06-27 03:00:00        17.0             28.0\n",
      "2014-06-27 04:00:00         6.0              8.0\n",
      "...                         ...              ...\n",
      "2014-07-03 19:00:00       153.0              NaN\n",
      "2014-07-03 20:00:00       206.0              NaN\n",
      "2014-07-03 21:00:00       237.0              NaN\n",
      "2014-07-03 22:00:00       250.0              NaN\n",
      "2014-07-03 23:00:00       295.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-07-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-07-04 00:00:00       291.0              NaN\n",
      "2014-07-04 01:00:00       299.0              NaN\n",
      "2014-07-04 02:00:00       303.0              NaN\n",
      "2014-07-04 03:00:00       293.0              NaN\n",
      "2014-07-04 04:00:00       281.0              NaN\n",
      "...                         ...              ...\n",
      "2014-07-10 19:00:00        28.0             28.0\n",
      "2014-07-10 20:00:00        31.0             25.0\n",
      "2014-07-10 21:00:00        40.0             31.0\n",
      "2014-07-10 22:00:00        42.0             36.0\n",
      "2014-07-10 23:00:00        48.0             44.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-07-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-07-11 00:00:00        52.0             52.0\n",
      "2014-07-11 01:00:00        53.0             54.0\n",
      "2014-07-11 02:00:00        58.0             53.0\n",
      "2014-07-11 03:00:00        58.0             48.0\n",
      "2014-07-11 04:00:00        64.0             53.0\n",
      "...                         ...              ...\n",
      "2014-07-17 19:00:00       160.0            145.0\n",
      "2014-07-17 20:00:00       172.0            152.0\n",
      "2014-07-17 21:00:00       188.0            160.0\n",
      "2014-07-17 22:00:00       195.0            169.0\n",
      "2014-07-17 23:00:00       220.0            184.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-07-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-07-18 00:00:00       191.0            188.0\n",
      "2014-07-18 01:00:00       154.0            151.0\n",
      "2014-07-18 02:00:00       158.0            137.0\n",
      "2014-07-18 03:00:00       131.0            122.0\n",
      "2014-07-18 04:00:00       128.0            108.0\n",
      "...                         ...              ...\n",
      "2014-07-24 19:00:00        33.0             31.0\n",
      "2014-07-24 20:00:00        35.0             32.0\n",
      "2014-07-24 21:00:00        63.0             57.0\n",
      "2014-07-24 22:00:00       104.0             82.0\n",
      "2014-07-24 23:00:00       136.0            120.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-07-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-07-25 00:00:00       146.0            109.0\n",
      "2014-07-25 01:00:00       118.0            107.0\n",
      "2014-07-25 02:00:00       127.0            110.0\n",
      "2014-07-25 03:00:00       149.0            120.0\n",
      "2014-07-25 04:00:00       160.0            123.0\n",
      "...                         ...              ...\n",
      "2014-07-31 19:00:00       135.0            136.0\n",
      "2014-07-31 20:00:00       167.0            157.0\n",
      "2014-07-31 21:00:00       187.0            179.0\n",
      "2014-07-31 22:00:00       206.0            193.0\n",
      "2014-07-31 23:00:00       196.0            188.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-08-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-08-01 00:00:00       154.0            153.0\n",
      "2014-08-01 01:00:00       120.0            110.0\n",
      "2014-08-01 02:00:00       135.0            117.0\n",
      "2014-08-01 03:00:00       132.0            118.0\n",
      "2014-08-01 04:00:00       136.0            123.0\n",
      "...                         ...              ...\n",
      "2014-08-07 19:00:00        64.0             54.0\n",
      "2014-08-07 20:00:00        48.0             51.0\n",
      "2014-08-07 21:00:00        56.0             51.0\n",
      "2014-08-07 22:00:00        41.0             48.0\n",
      "2014-08-07 23:00:00        48.0             42.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-08-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-08-08 00:00:00        29.0             41.0\n",
      "2014-08-08 01:00:00        40.0             44.0\n",
      "2014-08-08 02:00:00        42.0             48.0\n",
      "2014-08-08 03:00:00        47.0             48.0\n",
      "2014-08-08 04:00:00        51.0             49.0\n",
      "...                         ...              ...\n",
      "2014-08-14 19:00:00        38.0             38.0\n",
      "2014-08-14 20:00:00        37.0             32.0\n",
      "2014-08-14 21:00:00        26.0             33.0\n",
      "2014-08-14 22:00:00        17.0             20.0\n",
      "2014-08-14 23:00:00        32.0             24.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-08-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-08-15 00:00:00        26.0             20.0\n",
      "2014-08-15 01:00:00        34.0             29.0\n",
      "2014-08-15 02:00:00        32.0             36.0\n",
      "2014-08-15 03:00:00        23.0             33.0\n",
      "2014-08-15 04:00:00        35.0             27.0\n",
      "...                         ...              ...\n",
      "2014-08-21 19:00:00       145.0            132.0\n",
      "2014-08-21 20:00:00        69.0            103.0\n",
      "2014-08-21 21:00:00        19.0             17.0\n",
      "2014-08-21 22:00:00        26.0             21.0\n",
      "2014-08-21 23:00:00        22.0             24.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-08-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-08-22 00:00:00        23.0             17.0\n",
      "2014-08-22 01:00:00        21.0             20.0\n",
      "2014-08-22 02:00:00        23.0             23.0\n",
      "2014-08-22 03:00:00        18.0             20.0\n",
      "2014-08-22 04:00:00        24.0             21.0\n",
      "...                         ...              ...\n",
      "2014-08-28 19:00:00       129.0             99.0\n",
      "2014-08-28 20:00:00       125.0            116.0\n",
      "2014-08-28 21:00:00        74.0             78.0\n",
      "2014-08-28 22:00:00        58.0             54.0\n",
      "2014-08-28 23:00:00        79.0             65.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-08-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-08-29 00:00:00        77.0             67.0\n",
      "2014-08-29 01:00:00        80.0             63.0\n",
      "2014-08-29 02:00:00        85.0             70.0\n",
      "2014-08-29 03:00:00        74.0             65.0\n",
      "2014-08-29 04:00:00        78.0             66.0\n",
      "...                         ...              ...\n",
      "2014-09-04 19:00:00        38.0             37.0\n",
      "2014-09-04 20:00:00        60.0             48.0\n",
      "2014-09-04 21:00:00        49.0             54.0\n",
      "2014-09-04 22:00:00        52.0             52.0\n",
      "2014-09-04 23:00:00        60.0             54.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-09-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-09-05 00:00:00        61.0             58.0\n",
      "2014-09-05 01:00:00        64.0             62.0\n",
      "2014-09-05 02:00:00        74.0             64.0\n",
      "2014-09-05 03:00:00        80.0             62.0\n",
      "2014-09-05 04:00:00        76.0             71.0\n",
      "...                         ...              ...\n",
      "2014-09-11 19:00:00       124.0            114.0\n",
      "2014-09-11 20:00:00       113.0            105.0\n",
      "2014-09-11 21:00:00       104.0            100.0\n",
      "2014-09-11 22:00:00       106.0             96.0\n",
      "2014-09-11 23:00:00       121.0             95.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-09-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-09-12 00:00:00       124.0            107.0\n",
      "2014-09-12 01:00:00       135.0            120.0\n",
      "2014-09-12 02:00:00       147.0            123.0\n",
      "2014-09-12 03:00:00        82.0             82.0\n",
      "2014-09-12 04:00:00        60.0             57.0\n",
      "...                         ...              ...\n",
      "2014-09-18 19:00:00        37.0             37.0\n",
      "2014-09-18 20:00:00        51.0             45.0\n",
      "2014-09-18 21:00:00        61.0             57.0\n",
      "2014-09-18 22:00:00        63.0             64.0\n",
      "2014-09-18 23:00:00        59.0             63.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-09-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-09-19 00:00:00        70.0             60.0\n",
      "2014-09-19 01:00:00        78.0             74.0\n",
      "2014-09-19 02:00:00        84.0             81.0\n",
      "2014-09-19 03:00:00        97.0             91.0\n",
      "2014-09-19 04:00:00       110.0            103.0\n",
      "...                         ...              ...\n",
      "2014-09-25 19:00:00       147.0            118.0\n",
      "2014-09-25 20:00:00       157.0            120.0\n",
      "2014-09-25 21:00:00       145.0            117.0\n",
      "2014-09-25 22:00:00        74.0             83.0\n",
      "2014-09-25 23:00:00        60.0             63.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-09-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-09-26 00:00:00        75.0             66.0\n",
      "2014-09-26 01:00:00        86.0             79.0\n",
      "2014-09-26 02:00:00        87.0             86.0\n",
      "2014-09-26 03:00:00        99.0             88.0\n",
      "2014-09-26 04:00:00        96.0             92.0\n",
      "...                         ...              ...\n",
      "2014-10-02 19:00:00        79.0             49.0\n",
      "2014-10-02 20:00:00        93.0             73.0\n",
      "2014-10-02 21:00:00       129.0             91.0\n",
      "2014-10-02 22:00:00       132.0            111.0\n",
      "2014-10-02 23:00:00       121.0            110.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-10-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-10-03 00:00:00       115.0            102.0\n",
      "2014-10-03 01:00:00       113.0             96.0\n",
      "2014-10-03 02:00:00       135.0             85.0\n",
      "2014-10-03 03:00:00       115.0             79.0\n",
      "2014-10-03 04:00:00       149.0             79.0\n",
      "...                         ...              ...\n",
      "2014-10-09 19:00:00       443.0            415.0\n",
      "2014-10-09 20:00:00       438.0            422.0\n",
      "2014-10-09 21:00:00       420.0            429.0\n",
      "2014-10-09 22:00:00       454.0            399.0\n",
      "2014-10-09 23:00:00       430.0            398.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-10-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-10-10 00:00:00       396.0            396.0\n",
      "2014-10-10 01:00:00       386.0            377.0\n",
      "2014-10-10 02:00:00       375.0            362.0\n",
      "2014-10-10 03:00:00       344.0            345.0\n",
      "2014-10-10 04:00:00       348.0            321.0\n",
      "...                         ...              ...\n",
      "2014-10-16 19:00:00        55.0             52.0\n",
      "2014-10-16 20:00:00        60.0             60.0\n",
      "2014-10-16 21:00:00        70.0             67.0\n",
      "2014-10-16 22:00:00        80.0             79.0\n",
      "2014-10-16 23:00:00        85.0             85.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-10-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-10-17 00:00:00        88.0             86.0\n",
      "2014-10-17 01:00:00        91.0             84.0\n",
      "2014-10-17 02:00:00        98.0             89.0\n",
      "2014-10-17 03:00:00       103.0             93.0\n",
      "2014-10-17 04:00:00       101.0             96.0\n",
      "...                         ...              ...\n",
      "2014-10-23 19:00:00       191.0            168.0\n",
      "2014-10-23 20:00:00       196.0            167.0\n",
      "2014-10-23 21:00:00       195.0            183.0\n",
      "2014-10-23 22:00:00       214.0            201.0\n",
      "2014-10-23 23:00:00       227.0            205.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-10-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-10-24 00:00:00       217.0            201.0\n",
      "2014-10-24 01:00:00       195.0            208.0\n",
      "2014-10-24 02:00:00       183.0            185.0\n",
      "2014-10-24 03:00:00       179.0            179.0\n",
      "2014-10-24 04:00:00       191.0            168.0\n",
      "...                         ...              ...\n",
      "2014-10-30 19:00:00       212.0            213.0\n",
      "2014-10-30 20:00:00       193.0            219.0\n",
      "2014-10-30 21:00:00       205.0              NaN\n",
      "2014-10-30 22:00:00       206.0              NaN\n",
      "2014-10-30 23:00:00       212.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-10-31 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-10-31 00:00:00       216.0              NaN\n",
      "2014-10-31 01:00:00       232.0              NaN\n",
      "2014-10-31 02:00:00       223.0              NaN\n",
      "2014-10-31 03:00:00       229.0              NaN\n",
      "2014-10-31 04:00:00       206.0              NaN\n",
      "...                         ...              ...\n",
      "2014-11-06 19:00:00        40.0             25.0\n",
      "2014-11-06 20:00:00        40.0             34.0\n",
      "2014-11-06 21:00:00        48.0             42.0\n",
      "2014-11-06 22:00:00        44.0             44.0\n",
      "2014-11-06 23:00:00        41.0             43.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-11-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-11-07 00:00:00        44.0             46.0\n",
      "2014-11-07 01:00:00        45.0             43.0\n",
      "2014-11-07 02:00:00        79.0             63.0\n",
      "2014-11-07 03:00:00        76.0             79.0\n",
      "2014-11-07 04:00:00        79.0             76.0\n",
      "...                         ...              ...\n",
      "2014-11-13 19:00:00        33.0             35.0\n",
      "2014-11-13 20:00:00        45.0             44.0\n",
      "2014-11-13 21:00:00        53.0             55.0\n",
      "2014-11-13 22:00:00        44.0             45.0\n",
      "2014-11-13 23:00:00        43.0             49.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-11-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-11-14 00:00:00        52.0             43.0\n",
      "2014-11-14 01:00:00        52.0             42.0\n",
      "2014-11-14 02:00:00        52.0             56.0\n",
      "2014-11-14 03:00:00        54.0             59.0\n",
      "2014-11-14 04:00:00        52.0             49.0\n",
      "...                         ...              ...\n",
      "2014-11-20 19:00:00       375.0            349.0\n",
      "2014-11-20 20:00:00         NaN            584.0\n",
      "2014-11-20 21:00:00         NaN            602.0\n",
      "2014-11-20 22:00:00         NaN            623.0\n",
      "2014-11-20 23:00:00         NaN              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-11-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-11-21 00:00:00         NaN            638.0\n",
      "2014-11-21 01:00:00         NaN            602.0\n",
      "2014-11-21 02:00:00         NaN              NaN\n",
      "2014-11-21 03:00:00         NaN              NaN\n",
      "2014-11-21 04:00:00         NaN            394.0\n",
      "...                         ...              ...\n",
      "2014-11-27 19:00:00        24.0             28.0\n",
      "2014-11-27 20:00:00        34.0             26.0\n",
      "2014-11-27 21:00:00        37.0             36.0\n",
      "2014-11-27 22:00:00        39.0             33.0\n",
      "2014-11-27 23:00:00        34.0             30.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-11-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-11-28 00:00:00        39.0             34.0\n",
      "2014-11-28 01:00:00        49.0             39.0\n",
      "2014-11-28 02:00:00        51.0             37.0\n",
      "2014-11-28 03:00:00        52.0             63.0\n",
      "2014-11-28 04:00:00        70.0             77.0\n",
      "...                         ...              ...\n",
      "2014-12-04 19:00:00        17.0             12.0\n",
      "2014-12-04 20:00:00         9.0             12.0\n",
      "2014-12-04 21:00:00        18.0              9.0\n",
      "2014-12-04 22:00:00        10.0             17.0\n",
      "2014-12-04 23:00:00         6.0             10.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-12-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-12-05 00:00:00         6.0              6.0\n",
      "2014-12-05 01:00:00         9.0              9.0\n",
      "2014-12-05 02:00:00         8.0              7.0\n",
      "2014-12-05 03:00:00         7.0              7.0\n",
      "2014-12-05 04:00:00         8.0             13.0\n",
      "...                         ...              ...\n",
      "2014-12-11 19:00:00        81.0             75.0\n",
      "2014-12-11 20:00:00        32.0             52.0\n",
      "2014-12-11 21:00:00         9.0              8.0\n",
      "2014-12-11 22:00:00        12.0              5.0\n",
      "2014-12-11 23:00:00         8.0              8.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-12-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-12-12 00:00:00         8.0             11.0\n",
      "2014-12-12 01:00:00         8.0              6.0\n",
      "2014-12-12 02:00:00         9.0              6.0\n",
      "2014-12-12 03:00:00        12.0              8.0\n",
      "2014-12-12 04:00:00        14.0             10.0\n",
      "...                         ...              ...\n",
      "2014-12-18 19:00:00       240.0            214.0\n",
      "2014-12-18 20:00:00       235.0            215.0\n",
      "2014-12-18 21:00:00       252.0              NaN\n",
      "2014-12-18 22:00:00       254.0              NaN\n",
      "2014-12-18 23:00:00       274.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-12-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-12-19 00:00:00       253.0              NaN\n",
      "2014-12-19 01:00:00       248.0              NaN\n",
      "2014-12-19 02:00:00       219.0              NaN\n",
      "2014-12-19 03:00:00       199.0            195.0\n",
      "2014-12-19 04:00:00       198.0            190.0\n",
      "...                         ...              ...\n",
      "2014-12-25 19:00:00        52.0             41.0\n",
      "2014-12-25 20:00:00        50.0             40.0\n",
      "2014-12-25 21:00:00        51.0             44.0\n",
      "2014-12-25 22:00:00        69.0             53.0\n",
      "2014-12-25 23:00:00        93.0             62.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2014-12-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2014-12-26 00:00:00        94.0             86.0\n",
      "2014-12-26 01:00:00       147.0            102.0\n",
      "2014-12-26 02:00:00       226.0            131.0\n",
      "2014-12-26 03:00:00       179.0            173.0\n",
      "2014-12-26 04:00:00       153.0            156.0\n",
      "...                         ...              ...\n",
      "2015-01-01 19:00:00       106.0             88.0\n",
      "2015-01-01 20:00:00       123.0            100.0\n",
      "2015-01-01 21:00:00       136.0            102.0\n",
      "2015-01-01 22:00:00       139.0            124.0\n",
      "2015-01-01 23:00:00       154.0            134.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-01-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-01-02 00:00:00       126.0            126.0\n",
      "2015-01-02 01:00:00        98.0             98.0\n",
      "2015-01-02 02:00:00        66.0             68.0\n",
      "2015-01-02 03:00:00        45.0             47.0\n",
      "2015-01-02 04:00:00        28.0             27.0\n",
      "...                         ...              ...\n",
      "2015-01-08 19:00:00       210.0            189.0\n",
      "2015-01-08 20:00:00       358.0            327.0\n",
      "2015-01-08 21:00:00       359.0            383.0\n",
      "2015-01-08 22:00:00       356.0            379.0\n",
      "2015-01-08 23:00:00       259.0            367.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-01-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-01-09 00:00:00       226.0            309.0\n",
      "2015-01-09 01:00:00       294.0            245.0\n",
      "2015-01-09 02:00:00       250.0            252.0\n",
      "2015-01-09 03:00:00       215.0            251.0\n",
      "2015-01-09 04:00:00        55.0            127.0\n",
      "...                         ...              ...\n",
      "2015-01-15 19:00:00       423.0            428.0\n",
      "2015-01-15 20:00:00       447.0            450.0\n",
      "2015-01-15 21:00:00       461.0            428.0\n",
      "2015-01-15 22:00:00       470.0            460.0\n",
      "2015-01-15 23:00:00       483.0            497.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-01-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-01-16 00:00:00       484.0            512.0\n",
      "2015-01-16 01:00:00       502.0            513.0\n",
      "2015-01-16 02:00:00       490.0            501.0\n",
      "2015-01-16 03:00:00       355.0            490.0\n",
      "2015-01-16 04:00:00        31.0            149.0\n",
      "...                         ...              ...\n",
      "2015-01-22 19:00:00       178.0            190.0\n",
      "2015-01-22 20:00:00       171.0            184.0\n",
      "2015-01-22 21:00:00       167.0            174.0\n",
      "2015-01-22 22:00:00       168.0            175.0\n",
      "2015-01-22 23:00:00       235.0            174.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-01-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-01-23 00:00:00       300.0            221.0\n",
      "2015-01-23 01:00:00       292.0            289.0\n",
      "2015-01-23 02:00:00       253.0            293.0\n",
      "2015-01-23 03:00:00       217.0            253.0\n",
      "2015-01-23 04:00:00       196.0            207.0\n",
      "...                         ...              ...\n",
      "2015-01-29 19:00:00        14.0             12.0\n",
      "2015-01-29 20:00:00        12.0             15.0\n",
      "2015-01-29 21:00:00        14.0             12.0\n",
      "2015-01-29 22:00:00        11.0              8.0\n",
      "2015-01-29 23:00:00         9.0              8.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-01-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-01-30 00:00:00         5.0              7.0\n",
      "2015-01-30 01:00:00        10.0              8.0\n",
      "2015-01-30 02:00:00         8.0              9.0\n",
      "2015-01-30 03:00:00        10.0             10.0\n",
      "2015-01-30 04:00:00        11.0             10.0\n",
      "...                         ...              ...\n",
      "2015-02-05 19:00:00        58.0             61.0\n",
      "2015-02-05 20:00:00        60.0             64.0\n",
      "2015-02-05 21:00:00        65.0             66.0\n",
      "2015-02-05 22:00:00        63.0             68.0\n",
      "2015-02-05 23:00:00        60.0             60.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-02-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-02-06 00:00:00        55.0             65.0\n",
      "2015-02-06 01:00:00        53.0             61.0\n",
      "2015-02-06 02:00:00        58.0             67.0\n",
      "2015-02-06 03:00:00        89.0             77.0\n",
      "2015-02-06 04:00:00       105.0            108.0\n",
      "...                         ...              ...\n",
      "2015-02-12 19:00:00        53.0             47.0\n",
      "2015-02-12 20:00:00        73.0             76.0\n",
      "2015-02-12 21:00:00        75.0             83.0\n",
      "2015-02-12 22:00:00        88.0             90.0\n",
      "2015-02-12 23:00:00        73.0             76.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-02-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-02-13 00:00:00        61.0             70.0\n",
      "2015-02-13 01:00:00        68.0             69.0\n",
      "2015-02-13 02:00:00        94.0             75.0\n",
      "2015-02-13 03:00:00       111.0             91.0\n",
      "2015-02-13 04:00:00        96.0             95.0\n",
      "...                         ...              ...\n",
      "2015-02-19 19:00:00       125.0            127.0\n",
      "2015-02-19 20:00:00       130.0            129.0\n",
      "2015-02-19 21:00:00       111.0            121.0\n",
      "2015-02-19 22:00:00       129.0            111.0\n",
      "2015-02-19 23:00:00       127.0            121.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-02-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-02-20 00:00:00       129.0            123.0\n",
      "2015-02-20 01:00:00       116.0            118.0\n",
      "2015-02-20 02:00:00       120.0            110.0\n",
      "2015-02-20 03:00:00       112.0            118.0\n",
      "2015-02-20 04:00:00        81.0             92.0\n",
      "...                         ...              ...\n",
      "2015-02-26 19:00:00        15.0             22.0\n",
      "2015-02-26 20:00:00        18.0             18.0\n",
      "2015-02-26 21:00:00        16.0             22.0\n",
      "2015-02-26 22:00:00        11.0             26.0\n",
      "2015-02-26 23:00:00        12.0             14.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-02-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-02-27 00:00:00         8.0              9.0\n",
      "2015-02-27 01:00:00         8.0              6.0\n",
      "2015-02-27 02:00:00        14.0              7.0\n",
      "2015-02-27 03:00:00        10.0              3.0\n",
      "2015-02-27 04:00:00         8.0              3.0\n",
      "...                         ...              ...\n",
      "2015-03-05 19:00:00        93.0             95.0\n",
      "2015-03-05 20:00:00       140.0            145.0\n",
      "2015-03-05 21:00:00       162.0            180.0\n",
      "2015-03-05 22:00:00       153.0            169.0\n",
      "2015-03-05 23:00:00       142.0            127.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-03-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-03-06 00:00:00       118.0            116.0\n",
      "2015-03-06 01:00:00       144.0            133.0\n",
      "2015-03-06 02:00:00       201.0            184.0\n",
      "2015-03-06 03:00:00       181.0            190.0\n",
      "2015-03-06 04:00:00       178.0            181.0\n",
      "...                         ...              ...\n",
      "2015-03-12 19:00:00        90.0             80.0\n",
      "2015-03-12 20:00:00        86.0             93.0\n",
      "2015-03-12 21:00:00        84.0             79.0\n",
      "2015-03-12 22:00:00       111.0             94.0\n",
      "2015-03-12 23:00:00       125.0            118.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-03-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-03-13 00:00:00       146.0            143.0\n",
      "2015-03-13 01:00:00       134.0            136.0\n",
      "2015-03-13 02:00:00       123.0            122.0\n",
      "2015-03-13 03:00:00       110.0            112.0\n",
      "2015-03-13 04:00:00       111.0            106.0\n",
      "...                         ...              ...\n",
      "2015-03-19 19:00:00       100.0            111.0\n",
      "2015-03-19 20:00:00       122.0            127.0\n",
      "2015-03-19 21:00:00       136.0            150.0\n",
      "2015-03-19 22:00:00       138.0            148.0\n",
      "2015-03-19 23:00:00       116.0            127.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-03-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-03-20 00:00:00       120.0            119.0\n",
      "2015-03-20 01:00:00        78.0            106.0\n",
      "2015-03-20 02:00:00        53.0             91.0\n",
      "2015-03-20 03:00:00        37.0             36.0\n",
      "2015-03-20 04:00:00        28.0             36.0\n",
      "...                         ...              ...\n",
      "2015-03-26 19:00:00        89.0            235.0\n",
      "2015-03-26 20:00:00        69.0            369.0\n",
      "2015-03-26 21:00:00        75.0              NaN\n",
      "2015-03-26 22:00:00        70.0              NaN\n",
      "2015-03-26 23:00:00        60.0             55.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-03-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-03-27 00:00:00        56.0             59.0\n",
      "2015-03-27 01:00:00        58.0             56.0\n",
      "2015-03-27 02:00:00        62.0             63.0\n",
      "2015-03-27 03:00:00        69.0            116.0\n",
      "2015-03-27 04:00:00        69.0            205.0\n",
      "...                         ...              ...\n",
      "2015-04-02 19:00:00        17.0             20.0\n",
      "2015-04-02 20:00:00        18.0             17.0\n",
      "2015-04-02 21:00:00        12.0             13.0\n",
      "2015-04-02 22:00:00        12.0             17.0\n",
      "2015-04-02 23:00:00        11.0             13.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-04-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-04-03 00:00:00        15.0             14.0\n",
      "2015-04-03 01:00:00        20.0             21.0\n",
      "2015-04-03 02:00:00        21.0             23.0\n",
      "2015-04-03 03:00:00        27.0             20.0\n",
      "2015-04-03 04:00:00        19.0             21.0\n",
      "...                         ...              ...\n",
      "2015-04-09 19:00:00       213.0            221.0\n",
      "2015-04-09 20:00:00       232.0            237.0\n",
      "2015-04-09 21:00:00       232.0            242.0\n",
      "2015-04-09 22:00:00       238.0            246.0\n",
      "2015-04-09 23:00:00       237.0            243.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-04-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-04-10 00:00:00       239.0            248.0\n",
      "2015-04-10 01:00:00       222.0            251.0\n",
      "2015-04-10 02:00:00       238.0            230.0\n",
      "2015-04-10 03:00:00       236.0            245.0\n",
      "2015-04-10 04:00:00       224.0            229.0\n",
      "...                         ...              ...\n",
      "2015-04-16 19:00:00        32.0              NaN\n",
      "2015-04-16 20:00:00        34.0              NaN\n",
      "2015-04-16 21:00:00        44.0              NaN\n",
      "2015-04-16 22:00:00        43.0              NaN\n",
      "2015-04-16 23:00:00        43.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-04-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-04-17 00:00:00        45.0              NaN\n",
      "2015-04-17 01:00:00        45.0              NaN\n",
      "2015-04-17 02:00:00        50.0              NaN\n",
      "2015-04-17 03:00:00        51.0              NaN\n",
      "2015-04-17 04:00:00        42.0              NaN\n",
      "...                         ...              ...\n",
      "2015-04-23 19:00:00        14.0             14.0\n",
      "2015-04-23 20:00:00        10.0              8.0\n",
      "2015-04-23 21:00:00        24.0             15.0\n",
      "2015-04-23 22:00:00        23.0             18.0\n",
      "2015-04-23 23:00:00        26.0             27.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-04-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-04-24 00:00:00        78.0             67.0\n",
      "2015-04-24 01:00:00        70.0             66.0\n",
      "2015-04-24 02:00:00        56.0             52.0\n",
      "2015-04-24 03:00:00        61.0             49.0\n",
      "2015-04-24 04:00:00        60.0             53.0\n",
      "...                         ...              ...\n",
      "2015-04-30 19:00:00        69.0             68.0\n",
      "2015-04-30 20:00:00        94.0             90.0\n",
      "2015-04-30 21:00:00       187.0            185.0\n",
      "2015-04-30 22:00:00       226.0            219.0\n",
      "2015-04-30 23:00:00       244.0            236.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-05-01 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-05-01 00:00:00       260.0            253.0\n",
      "2015-05-01 01:00:00       261.0            257.0\n",
      "2015-05-01 02:00:00       248.0            248.0\n",
      "2015-05-01 03:00:00       210.0            206.0\n",
      "2015-05-01 04:00:00       166.0            171.0\n",
      "...                         ...              ...\n",
      "2015-05-07 19:00:00        56.0             58.0\n",
      "2015-05-07 20:00:00        56.0             53.0\n",
      "2015-05-07 21:00:00        58.0             60.0\n",
      "2015-05-07 22:00:00        19.0             56.0\n",
      "2015-05-07 23:00:00        19.0             18.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-05-08 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-05-08 00:00:00        20.0             16.0\n",
      "2015-05-08 01:00:00        20.0             18.0\n",
      "2015-05-08 02:00:00        27.0             20.0\n",
      "2015-05-08 03:00:00        18.0             23.0\n",
      "2015-05-08 04:00:00        21.0             18.0\n",
      "...                         ...              ...\n",
      "2015-05-14 19:00:00        32.0             30.0\n",
      "2015-05-14 20:00:00        28.0             36.0\n",
      "2015-05-14 21:00:00        44.0             36.0\n",
      "2015-05-14 22:00:00        30.0             39.0\n",
      "2015-05-14 23:00:00        26.0             31.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-05-15 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-05-15 00:00:00        11.0             21.0\n",
      "2015-05-15 01:00:00        16.0             15.0\n",
      "2015-05-15 02:00:00        17.0             18.0\n",
      "2015-05-15 03:00:00        16.0             16.0\n",
      "2015-05-15 04:00:00        59.0             18.0\n",
      "...                         ...              ...\n",
      "2015-05-21 19:00:00        34.0             19.0\n",
      "2015-05-21 20:00:00        30.0             28.0\n",
      "2015-05-21 21:00:00        37.0             28.0\n",
      "2015-05-21 22:00:00        35.0             33.0\n",
      "2015-05-21 23:00:00        43.0             35.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-05-22 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-05-22 00:00:00        36.0             31.0\n",
      "2015-05-22 01:00:00        42.0             34.0\n",
      "2015-05-22 02:00:00        41.0             40.0\n",
      "2015-05-22 03:00:00        48.0             38.0\n",
      "2015-05-22 04:00:00        48.0             42.0\n",
      "...                         ...              ...\n",
      "2015-05-28 19:00:00        87.0             77.0\n",
      "2015-05-28 20:00:00        81.0             72.0\n",
      "2015-05-28 21:00:00        70.0             72.0\n",
      "2015-05-28 22:00:00        83.0             79.0\n",
      "2015-05-28 23:00:00        83.0             77.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-05-29 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-05-29 00:00:00        72.0             60.0\n",
      "2015-05-29 01:00:00        71.0             52.0\n",
      "2015-05-29 02:00:00        79.0             76.0\n",
      "2015-05-29 03:00:00        70.0             72.0\n",
      "2015-05-29 04:00:00        74.0             70.0\n",
      "...                         ...              ...\n",
      "2015-06-04 19:00:00        70.0             78.0\n",
      "2015-06-04 20:00:00        61.0             71.0\n",
      "2015-06-04 21:00:00        68.0             71.0\n",
      "2015-06-04 22:00:00        49.0             53.0\n",
      "2015-06-04 23:00:00        42.0             43.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-06-05 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-06-05 00:00:00        31.0             40.0\n",
      "2015-06-05 01:00:00        27.0             40.0\n",
      "2015-06-05 02:00:00        37.0             42.0\n",
      "2015-06-05 03:00:00        35.0             39.0\n",
      "2015-06-05 04:00:00        49.0             39.0\n",
      "...                         ...              ...\n",
      "2015-06-11 19:00:00         9.0             11.0\n",
      "2015-06-11 20:00:00        20.0             15.0\n",
      "2015-06-11 21:00:00        26.0             16.0\n",
      "2015-06-11 22:00:00        46.0             26.0\n",
      "2015-06-11 23:00:00        48.0             40.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-06-12 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-06-12 00:00:00        24.0             32.0\n",
      "2015-06-12 01:00:00        19.0             24.0\n",
      "2015-06-12 02:00:00         3.0             16.0\n",
      "2015-06-12 03:00:00         5.0              9.0\n",
      "2015-06-12 04:00:00         3.0              4.0\n",
      "...                         ...              ...\n",
      "2015-06-18 19:00:00         8.0              9.0\n",
      "2015-06-18 20:00:00        27.0              7.0\n",
      "2015-06-18 21:00:00        16.0             13.0\n",
      "2015-06-18 22:00:00        11.0             14.0\n",
      "2015-06-18 23:00:00        11.0              7.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-06-19 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-06-19 00:00:00         8.0              7.0\n",
      "2015-06-19 01:00:00        10.0              9.0\n",
      "2015-06-19 02:00:00         9.0              6.0\n",
      "2015-06-19 03:00:00        12.0              6.0\n",
      "2015-06-19 04:00:00         8.0              5.0\n",
      "...                         ...              ...\n",
      "2015-06-25 19:00:00        73.0             87.0\n",
      "2015-06-25 20:00:00        78.0             89.0\n",
      "2015-06-25 21:00:00        85.0            101.0\n",
      "2015-06-25 22:00:00        99.0            114.0\n",
      "2015-06-25 23:00:00       110.0            124.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-06-26 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-06-26 00:00:00        94.0            120.0\n",
      "2015-06-26 01:00:00         NaN            118.0\n",
      "2015-06-26 02:00:00         NaN            103.0\n",
      "2015-06-26 03:00:00         NaN            104.0\n",
      "2015-06-26 04:00:00         NaN             93.0\n",
      "...                         ...              ...\n",
      "2015-07-02 19:00:00         7.0             10.0\n",
      "2015-07-02 20:00:00        10.0             14.0\n",
      "2015-07-02 21:00:00        32.0             14.0\n",
      "2015-07-02 22:00:00        30.0             19.0\n",
      "2015-07-02 23:00:00        19.0             20.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-07-03 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-07-03 00:00:00        27.0             30.0\n",
      "2015-07-03 01:00:00        31.0             24.0\n",
      "2015-07-03 02:00:00        24.0             22.0\n",
      "2015-07-03 03:00:00        22.0             25.0\n",
      "2015-07-03 04:00:00        33.0             27.0\n",
      "...                         ...              ...\n",
      "2015-07-09 19:00:00        32.0             28.0\n",
      "2015-07-09 20:00:00        29.0             21.0\n",
      "2015-07-09 21:00:00        25.0              9.0\n",
      "2015-07-09 22:00:00        24.0              8.0\n",
      "2015-07-09 23:00:00        26.0             25.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-07-10 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-07-10 00:00:00        20.0             16.0\n",
      "2015-07-10 01:00:00        13.0              5.0\n",
      "2015-07-10 02:00:00        24.0              8.0\n",
      "2015-07-10 03:00:00        41.0             29.0\n",
      "2015-07-10 04:00:00        45.0             37.0\n",
      "...                         ...              ...\n",
      "2015-07-16 19:00:00        29.0             20.0\n",
      "2015-07-16 20:00:00        28.0             27.0\n",
      "2015-07-16 21:00:00        38.0             26.0\n",
      "2015-07-16 22:00:00        48.0             36.0\n",
      "2015-07-16 23:00:00        44.0             50.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-07-17 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-07-17 00:00:00        37.0             39.0\n",
      "2015-07-17 01:00:00        18.0              NaN\n",
      "2015-07-17 02:00:00        13.0              NaN\n",
      "2015-07-17 03:00:00        16.0              NaN\n",
      "2015-07-17 04:00:00        25.0              NaN\n",
      "...                         ...              ...\n",
      "2015-07-23 19:00:00        38.0             25.0\n",
      "2015-07-23 20:00:00        33.0             23.0\n",
      "2015-07-23 21:00:00        35.0             24.0\n",
      "2015-07-23 22:00:00        48.0             32.0\n",
      "2015-07-23 23:00:00        58.0             39.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-07-24 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-07-24 00:00:00        51.0             48.0\n",
      "2015-07-24 01:00:00        42.0             52.0\n",
      "2015-07-24 02:00:00        41.0             40.0\n",
      "2015-07-24 03:00:00        44.0             37.0\n",
      "2015-07-24 04:00:00        48.0             40.0\n",
      "...                         ...              ...\n",
      "2015-07-30 19:00:00        15.0              4.0\n",
      "2015-07-30 20:00:00        18.0             17.0\n",
      "2015-07-30 21:00:00        12.0              9.0\n",
      "2015-07-30 22:00:00        24.0             15.0\n",
      "2015-07-30 23:00:00        19.0              8.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-07-31 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-07-31 00:00:00        14.0             10.0\n",
      "2015-07-31 01:00:00        23.0             15.0\n",
      "2015-07-31 02:00:00        18.0             17.0\n",
      "2015-07-31 03:00:00        12.0             12.0\n",
      "2015-07-31 04:00:00        18.0             16.0\n",
      "...                         ...              ...\n",
      "2015-08-06 19:00:00       111.0            109.0\n",
      "2015-08-06 20:00:00        96.0             86.0\n",
      "2015-08-06 21:00:00        96.0             87.0\n",
      "2015-08-06 22:00:00        71.0             76.0\n",
      "2015-08-06 23:00:00        80.0             81.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-08-07 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-08-07 00:00:00        94.0             85.0\n",
      "2015-08-07 01:00:00       101.0             97.0\n",
      "2015-08-07 02:00:00       119.0            108.0\n",
      "2015-08-07 03:00:00       120.0            114.0\n",
      "2015-08-07 04:00:00       105.0            116.0\n",
      "...                         ...              ...\n",
      "2015-08-13 19:00:00       109.0            110.0\n",
      "2015-08-13 20:00:00        98.0             96.0\n",
      "2015-08-13 21:00:00        90.0             90.0\n",
      "2015-08-13 22:00:00        81.0             79.0\n",
      "2015-08-13 23:00:00        93.0             84.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-08-14 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-08-14 00:00:00        95.0             96.0\n",
      "2015-08-14 01:00:00       105.0             98.0\n",
      "2015-08-14 02:00:00       124.0            117.0\n",
      "2015-08-14 03:00:00       128.0            138.0\n",
      "2015-08-14 04:00:00       141.0            132.0\n",
      "...                         ...              ...\n",
      "2015-08-20 19:00:00        45.0             39.0\n",
      "2015-08-20 20:00:00        52.0             47.0\n",
      "2015-08-20 21:00:00        40.0             42.0\n",
      "2015-08-20 22:00:00        19.0             10.0\n",
      "2015-08-20 23:00:00        14.0             12.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-08-21 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-08-21 00:00:00        16.0              4.0\n",
      "2015-08-21 01:00:00         8.0              NaN\n",
      "2015-08-21 02:00:00         5.0              NaN\n",
      "2015-08-21 03:00:00         6.0              NaN\n",
      "2015-08-21 04:00:00         9.0              NaN\n",
      "...                         ...              ...\n",
      "2015-08-27 19:00:00        15.0             15.0\n",
      "2015-08-27 20:00:00        18.0             16.0\n",
      "2015-08-27 21:00:00        16.0             16.0\n",
      "2015-08-27 22:00:00        26.0             18.0\n",
      "2015-08-27 23:00:00        38.0             21.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-08-28 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-08-28 00:00:00        37.0             23.0\n",
      "2015-08-28 01:00:00        32.0             22.0\n",
      "2015-08-28 02:00:00        27.0             25.0\n",
      "2015-08-28 03:00:00        17.0             20.0\n",
      "2015-08-28 04:00:00        12.0             15.0\n",
      "...                         ...              ...\n",
      "2015-09-03 19:00:00        17.0             19.0\n",
      "2015-09-03 20:00:00        25.0             19.0\n",
      "2015-09-03 21:00:00        32.0             27.0\n",
      "2015-09-03 22:00:00        39.0             30.0\n",
      "2015-09-03 23:00:00        41.0             39.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-09-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-09-04 00:00:00        51.0             50.0\n",
      "2015-09-04 01:00:00        55.0             55.0\n",
      "2015-09-04 02:00:00        62.0             53.0\n",
      "2015-09-04 03:00:00        63.0             61.0\n",
      "2015-09-04 04:00:00        60.0             60.0\n",
      "...                         ...              ...\n",
      "2015-09-10 19:00:00        11.0              NaN\n",
      "2015-09-10 20:00:00        10.0              3.0\n",
      "2015-09-10 21:00:00         8.0              3.0\n",
      "2015-09-10 22:00:00         8.0              3.0\n",
      "2015-09-10 23:00:00        13.0              3.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-09-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-09-11 00:00:00        13.0              3.0\n",
      "2015-09-11 01:00:00        13.0              4.0\n",
      "2015-09-11 02:00:00         7.0              NaN\n",
      "2015-09-11 03:00:00         9.0              NaN\n",
      "2015-09-11 04:00:00         7.0              3.0\n",
      "...                         ...              ...\n",
      "2015-09-17 19:00:00       120.0            133.0\n",
      "2015-09-17 20:00:00       131.0            150.0\n",
      "2015-09-17 21:00:00       151.0            157.0\n",
      "2015-09-17 22:00:00       153.0            165.0\n",
      "2015-09-17 23:00:00       175.0            163.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-09-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-09-18 00:00:00       201.0            190.0\n",
      "2015-09-18 01:00:00       206.0            227.0\n",
      "2015-09-18 02:00:00       201.0            230.0\n",
      "2015-09-18 03:00:00       185.0            214.0\n",
      "2015-09-18 04:00:00        92.0            128.0\n",
      "...                         ...              ...\n",
      "2015-09-24 19:00:00        22.0             23.0\n",
      "2015-09-24 20:00:00        23.0             26.0\n",
      "2015-09-24 21:00:00        25.0             29.0\n",
      "2015-09-24 22:00:00        16.0             20.0\n",
      "2015-09-24 23:00:00        18.0             18.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-09-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-09-25 00:00:00        20.0             26.0\n",
      "2015-09-25 01:00:00        29.0             31.0\n",
      "2015-09-25 02:00:00        26.0             28.0\n",
      "2015-09-25 03:00:00        22.0             27.0\n",
      "2015-09-25 04:00:00        19.0             27.0\n",
      "...                         ...              ...\n",
      "2015-10-01 19:00:00        18.0             15.0\n",
      "2015-10-01 20:00:00        21.0             23.0\n",
      "2015-10-01 21:00:00        19.0             20.0\n",
      "2015-10-01 22:00:00        26.0             25.0\n",
      "2015-10-01 23:00:00        22.0             25.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-10-02 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-10-02 00:00:00        24.0             29.0\n",
      "2015-10-02 01:00:00        23.0             27.0\n",
      "2015-10-02 02:00:00        21.0             27.0\n",
      "2015-10-02 03:00:00        19.0             22.0\n",
      "2015-10-02 04:00:00        13.0             19.0\n",
      "...                         ...              ...\n",
      "2015-10-08 19:00:00        14.0              NaN\n",
      "2015-10-08 20:00:00        14.0              NaN\n",
      "2015-10-08 21:00:00        14.0              NaN\n",
      "2015-10-08 22:00:00        25.0              NaN\n",
      "2015-10-08 23:00:00        37.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-10-09 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-10-09 00:00:00        37.0              NaN\n",
      "2015-10-09 01:00:00        33.0              NaN\n",
      "2015-10-09 02:00:00        33.0              NaN\n",
      "2015-10-09 03:00:00        24.0              NaN\n",
      "2015-10-09 04:00:00        22.0              NaN\n",
      "...                         ...              ...\n",
      "2015-10-15 19:00:00       141.0            145.0\n",
      "2015-10-15 20:00:00       165.0            169.0\n",
      "2015-10-15 21:00:00       189.0            189.0\n",
      "2015-10-15 22:00:00       203.0            195.0\n",
      "2015-10-15 23:00:00       172.0            200.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-10-16 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-10-16 00:00:00       157.0            193.0\n",
      "2015-10-16 01:00:00       156.0            203.0\n",
      "2015-10-16 02:00:00       164.0            190.0\n",
      "2015-10-16 03:00:00       152.0            177.0\n",
      "2015-10-16 04:00:00       113.0            161.0\n",
      "...                         ...              ...\n",
      "2015-10-22 19:00:00        31.0             16.0\n",
      "2015-10-22 20:00:00        30.0             23.0\n",
      "2015-10-22 21:00:00        32.0             36.0\n",
      "2015-10-22 22:00:00        29.0             29.0\n",
      "2015-10-22 23:00:00        30.0             28.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-10-23 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-10-23 00:00:00        38.0             35.0\n",
      "2015-10-23 01:00:00        41.0             41.0\n",
      "2015-10-23 02:00:00        38.0             36.0\n",
      "2015-10-23 03:00:00        36.0             40.0\n",
      "2015-10-23 04:00:00        33.0             39.0\n",
      "...                         ...              ...\n",
      "2015-10-29 19:00:00         9.0              6.0\n",
      "2015-10-29 20:00:00        10.0             13.0\n",
      "2015-10-29 21:00:00        12.0             10.0\n",
      "2015-10-29 22:00:00        10.0              8.0\n",
      "2015-10-29 23:00:00        16.0              4.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-10-30 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-10-30 00:00:00         7.0              3.0\n",
      "2015-10-30 01:00:00         7.0              6.0\n",
      "2015-10-30 02:00:00        16.0             17.0\n",
      "2015-10-30 03:00:00        13.0             10.0\n",
      "2015-10-30 04:00:00        15.0             19.0\n",
      "...                         ...              ...\n",
      "2015-11-05 19:00:00        24.0             26.0\n",
      "2015-11-05 20:00:00        24.0             18.0\n",
      "2015-11-05 21:00:00        18.0             18.0\n",
      "2015-11-05 22:00:00        16.0             18.0\n",
      "2015-11-05 23:00:00        17.0             17.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-11-06 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-11-06 00:00:00        17.0             15.0\n",
      "2015-11-06 01:00:00        20.0             14.0\n",
      "2015-11-06 02:00:00        17.0             11.0\n",
      "2015-11-06 03:00:00        18.0             19.0\n",
      "2015-11-06 04:00:00        21.0             16.0\n",
      "...                         ...              ...\n",
      "2015-11-12 19:00:00       192.0            199.0\n",
      "2015-11-12 20:00:00       221.0            197.0\n",
      "2015-11-12 21:00:00       217.0            217.0\n",
      "2015-11-12 22:00:00       212.0            221.0\n",
      "2015-11-12 23:00:00       196.0            209.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-11-13 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-11-13 00:00:00       206.0            216.0\n",
      "2015-11-13 01:00:00       184.0            204.0\n",
      "2015-11-13 02:00:00       172.0            173.0\n",
      "2015-11-13 03:00:00       157.0            162.0\n",
      "2015-11-13 04:00:00       156.0            161.0\n",
      "...                         ...              ...\n",
      "2015-11-19 19:00:00        80.0             70.0\n",
      "2015-11-19 20:00:00        91.0             78.0\n",
      "2015-11-19 21:00:00        88.0             85.0\n",
      "2015-11-19 22:00:00        90.0             82.0\n",
      "2015-11-19 23:00:00        74.0             74.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-11-20 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-11-20 00:00:00        63.0             67.0\n",
      "2015-11-20 01:00:00        60.0             63.0\n",
      "2015-11-20 02:00:00        59.0             61.0\n",
      "2015-11-20 03:00:00        50.0             53.0\n",
      "2015-11-20 04:00:00        45.0             44.0\n",
      "...                         ...              ...\n",
      "2015-11-26 19:00:00        60.0             65.0\n",
      "2015-11-26 20:00:00       112.0            100.0\n",
      "2015-11-26 21:00:00       138.0            123.0\n",
      "2015-11-26 22:00:00       134.0            125.0\n",
      "2015-11-26 23:00:00       126.0            125.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-11-27 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-11-27 00:00:00       150.0            153.0\n",
      "2015-11-27 01:00:00       171.0            160.0\n",
      "2015-11-27 02:00:00       171.0            165.0\n",
      "2015-11-27 03:00:00       185.0            175.0\n",
      "2015-11-27 04:00:00       168.0            183.0\n",
      "...                         ...              ...\n",
      "2015-12-03 19:00:00        14.0             10.0\n",
      "2015-12-03 20:00:00        17.0             15.0\n",
      "2015-12-03 21:00:00        14.0             15.0\n",
      "2015-12-03 22:00:00        14.0             12.0\n",
      "2015-12-03 23:00:00        11.0              9.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-12-04 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-12-04 00:00:00        17.0             14.0\n",
      "2015-12-04 01:00:00         9.0             14.0\n",
      "2015-12-04 02:00:00         9.0              6.0\n",
      "2015-12-04 03:00:00        10.0              5.0\n",
      "2015-12-04 04:00:00        10.0              6.0\n",
      "...                         ...              ...\n",
      "2015-12-10 19:00:00        35.0             35.0\n",
      "2015-12-10 20:00:00        23.0             24.0\n",
      "2015-12-10 21:00:00        19.0             27.0\n",
      "2015-12-10 22:00:00        23.0             21.0\n",
      "2015-12-10 23:00:00        25.0             22.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-12-11 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-12-11 00:00:00        18.0             19.0\n",
      "2015-12-11 01:00:00        21.0             20.0\n",
      "2015-12-11 02:00:00        21.0             17.0\n",
      "2015-12-11 03:00:00        19.0             20.0\n",
      "2015-12-11 04:00:00        14.0             19.0\n",
      "...                         ...              ...\n",
      "2015-12-17 19:00:00       118.0            118.0\n",
      "2015-12-17 20:00:00       126.0            121.0\n",
      "2015-12-17 21:00:00       128.0            122.0\n",
      "2015-12-17 22:00:00       139.0            122.0\n",
      "2015-12-17 23:00:00       145.0            120.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-12-18 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-12-18 00:00:00       152.0            123.0\n",
      "2015-12-18 01:00:00       169.0            165.0\n",
      "2015-12-18 02:00:00       194.0            183.0\n",
      "2015-12-18 03:00:00       191.0            205.0\n",
      "2015-12-18 04:00:00       113.0            179.0\n",
      "...                         ...              ...\n",
      "2015-12-24 19:00:00        51.0             47.0\n",
      "2015-12-24 20:00:00       195.0            131.0\n",
      "2015-12-24 21:00:00       360.0            348.0\n",
      "2015-12-24 22:00:00       378.0            432.0\n",
      "2015-12-24 23:00:00       497.0            511.0\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "2015-12-25 00:00:00\n",
      "                     PM_US Post  PM_Nongzhanguan\n",
      "datetime                                        \n",
      "2015-12-25 00:00:00       512.0            543.0\n",
      "2015-12-25 01:00:00       574.0              NaN\n",
      "2015-12-25 02:00:00       620.0            584.0\n",
      "2015-12-25 03:00:00       515.0            520.0\n",
      "2015-12-25 04:00:00       483.0            470.0\n",
      "...                         ...              ...\n",
      "2015-12-31 19:00:00       133.0            122.0\n",
      "2015-12-31 20:00:00       169.0            149.0\n",
      "2015-12-31 21:00:00       203.0            196.0\n",
      "2015-12-31 22:00:00       212.0            221.0\n",
      "2015-12-31 23:00:00       235.0              NaN\n",
      "\n",
      "[168 rows x 2 columns]\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in df.loc[:,[\"PM_US Post\",\"PM_Nongzhanguan\"]].resample(\"7D\"):\n",
    "    print(i[0])\n",
    "    print(i[1])\n",
    "    print(\"--\"*50)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T06:18:10.533075Z",
     "start_time": "2024-05-02T06:18:09.694223700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 取出要统计的两列，按7天分组，取平均值"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "            PM_US Post  PM_Nongzhanguan\ndatetime                               \n2010-01-01   71.627586              NaN\n2010-01-08   69.910714              NaN\n2010-01-15  163.654762              NaN\n2010-01-22   68.069307              NaN\n2010-01-29   53.583333              NaN\n...                ...              ...\n2015-11-27  242.642857       246.585366\n2015-12-04  145.437500       155.072289\n2015-12-11   88.750000        90.367470\n2015-12-18  204.139241       201.128049\n2015-12-25  209.244048       199.566265\n\n[313 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PM_US Post</th>\n      <th>PM_Nongzhanguan</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2010-01-01</th>\n      <td>71.627586</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2010-01-08</th>\n      <td>69.910714</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2010-01-15</th>\n      <td>163.654762</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2010-01-22</th>\n      <td>68.069307</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2010-01-29</th>\n      <td>53.583333</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015-11-27</th>\n      <td>242.642857</td>\n      <td>246.585366</td>\n    </tr>\n    <tr>\n      <th>2015-12-04</th>\n      <td>145.437500</td>\n      <td>155.072289</td>\n    </tr>\n    <tr>\n      <th>2015-12-11</th>\n      <td>88.750000</td>\n      <td>90.367470</td>\n    </tr>\n    <tr>\n      <th>2015-12-18</th>\n      <td>204.139241</td>\n      <td>201.128049</td>\n    </tr>\n    <tr>\n      <th>2015-12-25</th>\n      <td>209.244048</td>\n      <td>199.566265</td>\n    </tr>\n  </tbody>\n</table>\n<p>313 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,[\"PM_US Post\",\"PM_Nongzhanguan\"]].resample(\"7D\").mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:35:25.066595700Z",
     "start_time": "2024-05-02T05:35:25.034182600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 只要该样本有一列为空，则丢弃该样本\n",
    "### 即只要该天有一个区域没有pm2.5值，则丢弃该天的数据"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "            PM_US Post  PM_Nongzhanguan\ndatetime                               \n2013-01-11  277.731707        65.333333\n2013-01-18  188.244048       161.054217\n2013-01-25  261.263473       224.006098\n2013-02-01   72.845238        61.145570\n2013-02-08  125.832335       110.478788\n...                ...              ...\n2015-11-27  242.642857       246.585366\n2015-12-04  145.437500       155.072289\n2015-12-11   88.750000        90.367470\n2015-12-18  204.139241       201.128049\n2015-12-25  209.244048       199.566265\n\n[155 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PM_US Post</th>\n      <th>PM_Nongzhanguan</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-01-11</th>\n      <td>277.731707</td>\n      <td>65.333333</td>\n    </tr>\n    <tr>\n      <th>2013-01-18</th>\n      <td>188.244048</td>\n      <td>161.054217</td>\n    </tr>\n    <tr>\n      <th>2013-01-25</th>\n      <td>261.263473</td>\n      <td>224.006098</td>\n    </tr>\n    <tr>\n      <th>2013-02-01</th>\n      <td>72.845238</td>\n      <td>61.145570</td>\n    </tr>\n    <tr>\n      <th>2013-02-08</th>\n      <td>125.832335</td>\n      <td>110.478788</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015-11-27</th>\n      <td>242.642857</td>\n      <td>246.585366</td>\n    </tr>\n    <tr>\n      <th>2015-12-04</th>\n      <td>145.437500</td>\n      <td>155.072289</td>\n    </tr>\n    <tr>\n      <th>2015-12-11</th>\n      <td>88.750000</td>\n      <td>90.367470</td>\n    </tr>\n    <tr>\n      <th>2015-12-18</th>\n      <td>204.139241</td>\n      <td>201.128049</td>\n    </tr>\n    <tr>\n      <th>2015-12-25</th>\n      <td>209.244048</td>\n      <td>199.566265</td>\n    </tr>\n  </tbody>\n</table>\n<p>155 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,[\"PM_US Post\",\"PM_Nongzhanguan\"]].resample(\"7D\").mean().dropna()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:31:53.848108700Z",
     "start_time": "2024-05-02T05:31:53.812838900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "            PM_US Post  PM_Nongzhanguan\ndatetime                               \n2013-01-11  277.731707        65.333333\n2013-01-18  188.244048       161.054217\n2013-01-25  261.263473       224.006098\n2013-02-01   72.845238        61.145570\n2013-02-08  125.832335       110.478788\n...                ...              ...\n2015-11-27  242.642857       246.585366\n2015-12-04  145.437500       155.072289\n2015-12-11   88.750000        90.367470\n2015-12-18  204.139241       201.128049\n2015-12-25  209.244048       199.566265\n\n[155 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PM_US Post</th>\n      <th>PM_Nongzhanguan</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-01-11</th>\n      <td>277.731707</td>\n      <td>65.333333</td>\n    </tr>\n    <tr>\n      <th>2013-01-18</th>\n      <td>188.244048</td>\n      <td>161.054217</td>\n    </tr>\n    <tr>\n      <th>2013-01-25</th>\n      <td>261.263473</td>\n      <td>224.006098</td>\n    </tr>\n    <tr>\n      <th>2013-02-01</th>\n      <td>72.845238</td>\n      <td>61.145570</td>\n    </tr>\n    <tr>\n      <th>2013-02-08</th>\n      <td>125.832335</td>\n      <td>110.478788</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015-11-27</th>\n      <td>242.642857</td>\n      <td>246.585366</td>\n    </tr>\n    <tr>\n      <th>2015-12-04</th>\n      <td>145.437500</td>\n      <td>155.072289</td>\n    </tr>\n    <tr>\n      <th>2015-12-11</th>\n      <td>88.750000</td>\n      <td>90.367470</td>\n    </tr>\n    <tr>\n      <th>2015-12-18</th>\n      <td>204.139241</td>\n      <td>201.128049</td>\n    </tr>\n    <tr>\n      <th>2015-12-25</th>\n      <td>209.244048</td>\n      <td>199.566265</td>\n    </tr>\n  </tbody>\n</table>\n<p>155 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.loc[:,[\"PM_US Post\",\"PM_Nongzhanguan\"]].resample(\"7D\").mean().dropna()\n",
    "df1"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:36:50.449426900Z",
     "start_time": "2024-05-02T05:36:50.419510100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "datetime\n2013-01-11    277.731707\n2013-01-18    188.244048\n2013-01-25    261.263473\n2013-02-01     72.845238\n2013-02-08    125.832335\n                 ...    \n2015-11-27    242.642857\n2015-12-04    145.437500\n2015-12-11     88.750000\n2015-12-18    204.139241\n2015-12-25    209.244048\nFreq: 7D, Name: PM_US Post, Length: 155, dtype: float64"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_usa = df1.loc[:,\"PM_US Post\"]\n",
    "data_usa"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:38:23.367569200Z",
     "start_time": "2024-05-02T05:38:23.338731500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "datetime\n2013-01-11     65.333333\n2013-01-18    161.054217\n2013-01-25    224.006098\n2013-02-01     61.145570\n2013-02-08    110.478788\n                 ...    \n2015-11-27    246.585366\n2015-12-04    155.072289\n2015-12-11     90.367470\n2015-12-18    201.128049\n2015-12-25    199.566265\nFreq: 7D, Name: PM_Nongzhanguan, Length: 155, dtype: float64"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_china = df1.loc[:,\"PM_Nongzhanguan\"]\n",
    "data_china"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:38:40.565859200Z",
     "start_time": "2024-05-02T05:38:40.532942Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "DatetimeIndex: 155 entries, 2013-01-11 to 2015-12-25\n",
      "Freq: 7D\n",
      "Series name: PM_US Post\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "155 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.4 KB\n",
      "None\n",
      "====================================================================================================\n",
      "<class 'pandas.core.series.Series'>\n",
      "DatetimeIndex: 155 entries, 2013-01-11 to 2015-12-25\n",
      "Freq: 7D\n",
      "Series name: PM_Nongzhanguan\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "155 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.4 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data_usa.info())\n",
    "print(\"==\"*50)\n",
    "\n",
    "print(data_china.info())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:42:48.340111400Z",
     "start_time": "2024-05-02T05:42:48.307821800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2013-01-11', '2013-01-18', '2013-01-25', '2013-02-01',\n",
      "               '2013-02-08', '2013-02-15', '2013-02-22', '2013-03-01',\n",
      "               '2013-03-08', '2013-03-15',\n",
      "               ...\n",
      "               '2015-10-23', '2015-10-30', '2015-11-06', '2015-11-13',\n",
      "               '2015-11-20', '2015-11-27', '2015-12-04', '2015-12-11',\n",
      "               '2015-12-18', '2015-12-25'],\n",
      "              dtype='datetime64[ns]', name='datetime', length=155, freq='7D')\n",
      "----------------------------------------------------------------------------------------------------\n",
      "['2013-01-11', '2013-01-18', '2013-01-25', '2013-02-01', '2013-02-08', '2013-02-15', '2013-02-22', '2013-03-01', '2013-03-08', '2013-03-15', '2013-03-22', '2013-03-29', '2013-04-05', '2013-04-12', '2013-04-19', '2013-04-26', '2013-05-03', '2013-05-10', '2013-05-17', '2013-05-24', '2013-05-31', '2013-06-07', '2013-06-14', '2013-06-21', '2013-06-28', '2013-07-05', '2013-07-12', '2013-07-19', '2013-07-26', '2013-08-02', '2013-08-09', '2013-08-16', '2013-08-23', '2013-08-30', '2013-09-06', '2013-09-13', '2013-09-20', '2013-09-27', '2013-10-04', '2013-10-11', '2013-10-18', '2013-10-25', '2013-11-01', '2013-11-08', '2013-11-15', '2013-11-22', '2013-11-29', '2013-12-06', '2013-12-13', '2013-12-20', '2013-12-27', '2014-01-03', '2014-01-10', '2014-01-17', '2014-01-24', '2014-01-31', '2014-02-07', '2014-02-14', '2014-02-21', '2014-02-28', '2014-03-07', '2014-03-14', '2014-03-21', '2014-03-28', '2014-04-04', '2014-04-11', '2014-04-18', '2014-04-25', '2014-05-02', '2014-05-09', '2014-05-16', '2014-05-23', '2014-05-30', '2014-06-06', '2014-06-13', '2014-06-20', '2014-06-27', '2014-07-04', '2014-07-11', '2014-07-18', '2014-07-25', '2014-08-01', '2014-08-08', '2014-08-15', '2014-08-22', '2014-08-29', '2014-09-05', '2014-09-12', '2014-09-19', '2014-09-26', '2014-10-03', '2014-10-10', '2014-10-17', '2014-10-24', '2014-10-31', '2014-11-07', '2014-11-14', '2014-11-21', '2014-11-28', '2014-12-05', '2014-12-12', '2014-12-19', '2014-12-26', '2015-01-02', '2015-01-09', '2015-01-16', '2015-01-23', '2015-01-30', '2015-02-06', '2015-02-13', '2015-02-20', '2015-02-27', '2015-03-06', '2015-03-13', '2015-03-20', '2015-03-27', '2015-04-03', '2015-04-10', '2015-04-17', '2015-04-24', '2015-05-01', '2015-05-08', '2015-05-15', '2015-05-22', '2015-05-29', '2015-06-05', '2015-06-12', '2015-06-19', '2015-06-26', '2015-07-03', '2015-07-10', '2015-07-17', '2015-07-24', '2015-07-31', '2015-08-07', '2015-08-14', '2015-08-21', '2015-08-28', '2015-09-04', '2015-09-11', '2015-09-18', '2015-09-25', '2015-10-02', '2015-10-09', '2015-10-16', '2015-10-23', '2015-10-30', '2015-11-06', '2015-11-13', '2015-11-20', '2015-11-27', '2015-12-04', '2015-12-11', '2015-12-18', '2015-12-25']\n",
      "155 155\n",
      "DatetimeIndex(['2013-01-11', '2013-01-18', '2013-01-25', '2013-02-01',\n",
      "               '2013-02-08', '2013-02-15', '2013-02-22', '2013-03-01',\n",
      "               '2013-03-08', '2013-03-15',\n",
      "               ...\n",
      "               '2015-10-23', '2015-10-30', '2015-11-06', '2015-11-13',\n",
      "               '2015-11-20', '2015-11-27', '2015-12-04', '2015-12-11',\n",
      "               '2015-12-18', '2015-12-25'],\n",
      "              dtype='datetime64[ns]', name='datetime', length=155, freq='7D')\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "_x_china = data_china.index\n",
    "print(_x_china)\n",
    "print(\"--\"*50)\n",
    "\n",
    "_x_china = [i.strftime(\"%Y-%m-%d\") for i in _x_china]\n",
    "print(_x_china)\n",
    "\n",
    "print(len(_x_china),len(data_china))\n",
    "\n",
    "_x_usa = data_usa.index\n",
    "print(_x_usa)\n",
    "print(\"--\"*50)\n",
    "\n",
    "_x_usa = [i.strftime(\"%Y-%m-%d\") for i in _x_usa]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T05:55:18.573532800Z",
     "start_time": "2024-05-02T05:55:18.522553600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,8),dpi = 80)\n",
    "\n",
    "_y_usa = data_usa.values\n",
    "_y_china = data_china.values\n",
    "\n",
    "plt.plot(range(len(_x_china)), _y_china, label = \"CHINA\", alpha = 0.7)\n",
    "plt.plot(range(len(_x_usa)), _y_usa, label = \"USA\", alpha = 0.7)\n",
    "\n",
    "# 每10个刻度打一个标签，即每10周\n",
    "plt.xticks(range(0, len(_x_china), 10), _x_china[::10], rotation=45)\n",
    "\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T06:00:28.404900Z",
     "start_time": "2024-05-02T06:00:28.177979600Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
